Item talk:Q142209
From geokb
{
"OpenAlex": {
"id": "https://openalex.org/A5084145417",
"orcid": "https://orcid.org/0000-0002-3517-337X",
"display_name": "John Jakeman",
"display_name_alternatives": [
"John Davis Jakeman",
"J. Jakeman",
"John Jakeman",
"John D. Jakeman",
"J. D. Jakeman"
],
"works_count": 218,
"cited_by_count": 2469,
"summary_stats": {
"2yr_mean_citedness": 2.8125,
"h_index": 24,
"i10_index": 40
},
"ids": {
"openalex": "https://openalex.org/A5084145417",
"orcid": "https://orcid.org/0000-0002-3517-337X",
"scopus": "http://www.scopus.com/inward/authorDetails.url?authorID=35758908600&partnerID=MN8TOARS"
},
"affiliations": [
{
"institution": {
"id": "https://openalex.org/I4210104735",
"ror": "https://ror.org/01apwpt12",
"display_name": "Sandia National Laboratories",
"country_code": "US",
"type": "facility",
"lineage": [
"https://openalex.org/I1330989302",
"https://openalex.org/I198811213",
"https://openalex.org/I4210104735"
]
},
"years": [
2024,
2023,
2022,
2021,
2020,
2019,
2018,
2017,
2016,
2015
]
},
{
"institution": {
"id": "https://openalex.org/I192454743",
"ror": "https://ror.org/058m7ey48",
"display_name": "Sandia National Laboratories California",
"country_code": "US",
"type": "facility",
"lineage": [
"https://openalex.org/I1330989302",
"https://openalex.org/I192454743",
"https://openalex.org/I198811213",
"https://openalex.org/I4210104735"
]
},
"years": [
2024,
2023,
2022,
2021,
2020,
2019,
2018,
2017,
2015,
2014
]
},
{
"institution": {
"id": "https://openalex.org/I52357470",
"ror": "https://ror.org/00rs6vg23",
"display_name": "The Ohio State University",
"country_code": "US",
"type": "education",
"lineage": [
"https://openalex.org/I52357470"
]
},
"years": [
2020
]
},
{
"institution": {
"id": "https://openalex.org/I219193219",
"ror": "https://ror.org/02dqehb95",
"display_name": "Purdue University West Lafayette",
"country_code": "US",
"type": "education",
"lineage": [
"https://openalex.org/I219193219"
]
},
"years": [
2012
]
},
{
"institution": {
"id": "https://openalex.org/I2801333002",
"ror": "https://ror.org/05p8z3f47",
"display_name": "Purdue University System",
"country_code": "US",
"type": "education",
"lineage": [
"https://openalex.org/I2801333002"
]
},
"years": [
2011
]
},
{
"institution": {
"id": "https://openalex.org/I118347636",
"ror": "https://ror.org/019wvm592",
"display_name": "Australian National University",
"country_code": "AU",
"type": "education",
"lineage": [
"https://openalex.org/I118347636"
]
},
"years": [
2011,
2010,
2008,
2006
]
},
{
"institution": {
"id": "https://openalex.org/I2802631561",
"ror": "https://ror.org/00yn60108",
"display_name": "Australian Mathematical Sciences Institute",
"country_code": "AU",
"type": "other",
"lineage": [
"https://openalex.org/I2802631561"
]
},
"years": [
2008
]
}
],
"last_known_institutions": [
{
"id": "https://openalex.org/I192454743",
"ror": "https://ror.org/058m7ey48",
"display_name": "Sandia National Laboratories California",
"country_code": "US",
"type": "facility",
"lineage": [
"https://openalex.org/I1330989302",
"https://openalex.org/I192454743",
"https://openalex.org/I198811213",
"https://openalex.org/I4210104735"
]
}
],
"topics": [
{
"id": "https://openalex.org/T10928",
"display_name": "Uncertainty Quantification and Sensitivity Analysis",
"count": 78,
"subfield": {
"id": "https://openalex.org/subfields/1804",
"display_name": "Statistics, Probability and Uncertainty"
},
"field": {
"id": "https://openalex.org/fields/18",
"display_name": "Decision Sciences"
},
"domain": {
"id": "https://openalex.org/domains/2",
"display_name": "Social Sciences"
}
},
{
"id": "https://openalex.org/T10848",
"display_name": "Multiobjective Optimization in Evolutionary Algorithms",
"count": 35,
"subfield": {
"id": "https://openalex.org/subfields/1703",
"display_name": "Computational Theory and Mathematics"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11206",
"display_name": "Physics-Informed Neural Networks for Scientific Computing",
"count": 23,
"subfield": {
"id": "https://openalex.org/subfields/3109",
"display_name": "Statistical and Nonlinear Physics"
},
"field": {
"id": "https://openalex.org/fields/31",
"display_name": "Physics and Astronomy"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11195",
"display_name": "Optimization Techniques in Simulation Modeling",
"count": 16,
"subfield": {
"id": "https://openalex.org/subfields/1803",
"display_name": "Management Science and Operations Research"
},
"field": {
"id": "https://openalex.org/fields/18",
"display_name": "Decision Sciences"
},
"domain": {
"id": "https://openalex.org/domains/2",
"display_name": "Social Sciences"
}
},
{
"id": "https://openalex.org/T12814",
"display_name": "Gaussian Processes in Machine Learning",
"count": 15,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10876",
"display_name": "Process Fault Detection and Diagnosis in Industries",
"count": 15,
"subfield": {
"id": "https://openalex.org/subfields/2207",
"display_name": "Control and Systems Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10534",
"display_name": "Structural Health Monitoring Techniques",
"count": 14,
"subfield": {
"id": "https://openalex.org/subfields/2205",
"display_name": "Civil and Structural Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11798",
"display_name": "Experimental Design and Optimization Methods",
"count": 11,
"subfield": {
"id": "https://openalex.org/subfields/1803",
"display_name": "Management Science and Operations Research"
},
"field": {
"id": "https://openalex.org/fields/18",
"display_name": "Decision Sciences"
},
"domain": {
"id": "https://openalex.org/domains/2",
"display_name": "Social Sciences"
}
},
{
"id": "https://openalex.org/T10320",
"display_name": "Neural Network Fundamentals and Applications",
"count": 10,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10644",
"display_name": "Impacts of Climate Change on Glaciers and Water Availability",
"count": 9,
"subfield": {
"id": "https://openalex.org/subfields/1902",
"display_name": "Atmospheric Science"
},
"field": {
"id": "https://openalex.org/fields/19",
"display_name": "Earth and Planetary Sciences"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T13650",
"display_name": "Scientific Computing and Data Analysis with Python",
"count": 8,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T13126",
"display_name": "Theoretical and Computational Physics",
"count": 8,
"subfield": {
"id": "https://openalex.org/subfields/3109",
"display_name": "Statistical and Nonlinear Physics"
},
"field": {
"id": "https://openalex.org/fields/31",
"display_name": "Physics and Astronomy"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10894",
"display_name": "Groundwater Flow and Transport Modeling",
"count": 7,
"subfield": {
"id": "https://openalex.org/subfields/2305",
"display_name": "Environmental Engineering"
},
"field": {
"id": "https://openalex.org/fields/23",
"display_name": "Environmental Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11801",
"display_name": "Advanced Techniques in Reservoir Management",
"count": 7,
"subfield": {
"id": "https://openalex.org/subfields/2212",
"display_name": "Ocean Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11405",
"display_name": "Global Sea Level Variability and Change",
"count": 7,
"subfield": {
"id": "https://openalex.org/subfields/1910",
"display_name": "Oceanography"
},
"field": {
"id": "https://openalex.org/fields/19",
"display_name": "Earth and Planetary Sciences"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11159",
"display_name": "Design for Manufacture and Assembly in Manufacturing",
"count": 7,
"subfield": {
"id": "https://openalex.org/subfields/2209",
"display_name": "Industrial and Manufacturing Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T12535",
"display_name": "Learning with Noisy Labels in Machine Learning",
"count": 6,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11236",
"display_name": "System Identification Techniques",
"count": 6,
"subfield": {
"id": "https://openalex.org/subfields/2207",
"display_name": "Control and Systems Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T12404",
"display_name": "High-dimensional Integration and Quasi-Monte Carlo Methods",
"count": 5,
"subfield": {
"id": "https://openalex.org/subfields/2612",
"display_name": "Numerical Analysis"
},
"field": {
"id": "https://openalex.org/fields/26",
"display_name": "Mathematics"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10500",
"display_name": "Theory and Applications of Compressed Sensing",
"count": 5,
"subfield": {
"id": "https://openalex.org/subfields/2206",
"display_name": "Computational Mechanics"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10339",
"display_name": "Finite Element Methods for Fluid-Structure Interaction",
"count": 5,
"subfield": {
"id": "https://openalex.org/subfields/2206",
"display_name": "Computational Mechanics"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10688",
"display_name": "Image Denoising Techniques and Algorithms",
"count": 5,
"subfield": {
"id": "https://openalex.org/subfields/1707",
"display_name": "Computer Vision and Pattern Recognition"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10715",
"display_name": "Distributed Grid Computing Systems",
"count": 5,
"subfield": {
"id": "https://openalex.org/subfields/1705",
"display_name": "Computer Networks and Communications"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10763",
"display_name": "Industry 4.0 and Digital Transformation in Manufacturing",
"count": 4,
"subfield": {
"id": "https://openalex.org/subfields/2209",
"display_name": "Industrial and Manufacturing Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11459",
"display_name": "Arctic Sea Ice Variability and Decline",
"count": 4,
"subfield": {
"id": "https://openalex.org/subfields/1902",
"display_name": "Atmospheric Science"
},
"field": {
"id": "https://openalex.org/fields/19",
"display_name": "Earth and Planetary Sciences"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
}
],
"topic_share": [
{
"id": "https://openalex.org/T10928",
"display_name": "Uncertainty Quantification and Sensitivity Analysis",
"value": 0.0004429,
"subfield": {
"id": "https://openalex.org/subfields/1804",
"display_name": "Statistics, Probability and Uncertainty"
},
"field": {
"id": "https://openalex.org/fields/18",
"display_name": "Decision Sciences"
},
"domain": {
"id": "https://openalex.org/domains/2",
"display_name": "Social Sciences"
}
},
{
"id": "https://openalex.org/T12814",
"display_name": "Gaussian Processes in Machine Learning",
"value": 0.0003719,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10848",
"display_name": "Multiobjective Optimization in Evolutionary Algorithms",
"value": 0.0003351,
"subfield": {
"id": "https://openalex.org/subfields/1703",
"display_name": "Computational Theory and Mathematics"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11206",
"display_name": "Physics-Informed Neural Networks for Scientific Computing",
"value": 0.0002743,
"subfield": {
"id": "https://openalex.org/subfields/3109",
"display_name": "Statistical and Nonlinear Physics"
},
"field": {
"id": "https://openalex.org/fields/31",
"display_name": "Physics and Astronomy"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11798",
"display_name": "Experimental Design and Optimization Methods",
"value": 0.0001653,
"subfield": {
"id": "https://openalex.org/subfields/1803",
"display_name": "Management Science and Operations Research"
},
"field": {
"id": "https://openalex.org/fields/18",
"display_name": "Decision Sciences"
},
"domain": {
"id": "https://openalex.org/domains/2",
"display_name": "Social Sciences"
}
},
{
"id": "https://openalex.org/T11195",
"display_name": "Optimization Techniques in Simulation Modeling",
"value": 0.0001112,
"subfield": {
"id": "https://openalex.org/subfields/1803",
"display_name": "Management Science and Operations Research"
},
"field": {
"id": "https://openalex.org/fields/18",
"display_name": "Decision Sciences"
},
"domain": {
"id": "https://openalex.org/domains/2",
"display_name": "Social Sciences"
}
},
{
"id": "https://openalex.org/T12303",
"display_name": "Tensor Decompositions and Applications in Multilinear Algebra",
"value": 0.0001112,
"subfield": {
"id": "https://openalex.org/subfields/2605",
"display_name": "Computational Mathematics"
},
"field": {
"id": "https://openalex.org/fields/26",
"display_name": "Mathematics"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T12404",
"display_name": "High-dimensional Integration and Quasi-Monte Carlo Methods",
"value": 9.97e-05,
"subfield": {
"id": "https://openalex.org/subfields/2612",
"display_name": "Numerical Analysis"
},
"field": {
"id": "https://openalex.org/fields/26",
"display_name": "Mathematics"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11612",
"display_name": "Optimization Methods in Machine Learning",
"value": 8.63e-05,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T12535",
"display_name": "Learning with Noisy Labels in Machine Learning",
"value": 7.71e-05,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T14146",
"display_name": "Impact of Tsunami on Structures and Infrastructure",
"value": 6.96e-05,
"subfield": {
"id": "https://openalex.org/subfields/2205",
"display_name": "Civil and Structural Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T13141",
"display_name": "Multivariate Analysis in Statistical Research",
"value": 6.52e-05,
"subfield": {
"id": "https://openalex.org/subfields/2613",
"display_name": "Statistics and Probability"
},
"field": {
"id": "https://openalex.org/fields/26",
"display_name": "Mathematics"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T13650",
"display_name": "Scientific Computing and Data Analysis with Python",
"value": 5.5e-05,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T13126",
"display_name": "Theoretical and Computational Physics",
"value": 4.97e-05,
"subfield": {
"id": "https://openalex.org/subfields/3109",
"display_name": "Statistical and Nonlinear Physics"
},
"field": {
"id": "https://openalex.org/fields/31",
"display_name": "Physics and Astronomy"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10534",
"display_name": "Structural Health Monitoring Techniques",
"value": 4.81e-05,
"subfield": {
"id": "https://openalex.org/subfields/2205",
"display_name": "Civil and Structural Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10763",
"display_name": "Industry 4.0 and Digital Transformation in Manufacturing",
"value": 4.74e-05,
"subfield": {
"id": "https://openalex.org/subfields/2209",
"display_name": "Industrial and Manufacturing Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10876",
"display_name": "Process Fault Detection and Diagnosis in Industries",
"value": 4.59e-05,
"subfield": {
"id": "https://openalex.org/subfields/2207",
"display_name": "Control and Systems Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10500",
"display_name": "Theory and Applications of Compressed Sensing",
"value": 4.47e-05,
"subfield": {
"id": "https://openalex.org/subfields/2206",
"display_name": "Computational Mechanics"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11413",
"display_name": "Robust Optimization for Risk Management and Finance",
"value": 4.29e-05,
"subfield": {
"id": "https://openalex.org/subfields/1803",
"display_name": "Management Science and Operations Research"
},
"field": {
"id": "https://openalex.org/fields/18",
"display_name": "Decision Sciences"
},
"domain": {
"id": "https://openalex.org/domains/2",
"display_name": "Social Sciences"
}
},
{
"id": "https://openalex.org/T12696",
"display_name": "Icing Mitigation Techniques for Wind Turbines and Aircraft",
"value": 4.02e-05,
"subfield": {
"id": "https://openalex.org/subfields/2202",
"display_name": "Aerospace Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10894",
"display_name": "Groundwater Flow and Transport Modeling",
"value": 3.77e-05,
"subfield": {
"id": "https://openalex.org/subfields/2305",
"display_name": "Environmental Engineering"
},
"field": {
"id": "https://openalex.org/fields/23",
"display_name": "Environmental Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11236",
"display_name": "System Identification Techniques",
"value": 3.59e-05,
"subfield": {
"id": "https://openalex.org/subfields/2207",
"display_name": "Control and Systems Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10644",
"display_name": "Impacts of Climate Change on Glaciers and Water Availability",
"value": 3.53e-05,
"subfield": {
"id": "https://openalex.org/subfields/1902",
"display_name": "Atmospheric Science"
},
"field": {
"id": "https://openalex.org/fields/19",
"display_name": "Earth and Planetary Sciences"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T12056",
"display_name": "Bayesian Monte Carlo Methods in Scientific Inference",
"value": 3.41e-05,
"subfield": {
"id": "https://openalex.org/subfields/2613",
"display_name": "Statistics and Probability"
},
"field": {
"id": "https://openalex.org/fields/26",
"display_name": "Mathematics"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10339",
"display_name": "Finite Element Methods for Fluid-Structure Interaction",
"value": 3.15e-05,
"subfield": {
"id": "https://openalex.org/subfields/2206",
"display_name": "Computational Mechanics"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
}
],
"x_concepts": [
{
"id": "https://openalex.org/C41008148",
"wikidata": "https://www.wikidata.org/wiki/Q21198",
"display_name": "Computer science",
"level": 0,
"score": 95.0
},
{
"id": "https://openalex.org/C33923547",
"wikidata": "https://www.wikidata.org/wiki/Q395",
"display_name": "Mathematics",
"level": 0,
"score": 73.9
},
{
"id": "https://openalex.org/C105795698",
"wikidata": "https://www.wikidata.org/wiki/Q12483",
"display_name": "Statistics",
"level": 1,
"score": 53.2
},
{
"id": "https://openalex.org/C121332964",
"wikidata": "https://www.wikidata.org/wiki/Q413",
"display_name": "Physics",
"level": 0,
"score": 47.2
},
{
"id": "https://openalex.org/C127413603",
"wikidata": "https://www.wikidata.org/wiki/Q11023",
"display_name": "Engineering",
"level": 0,
"score": 45.9
},
{
"id": "https://openalex.org/C119857082",
"wikidata": "https://www.wikidata.org/wiki/Q2539",
"display_name": "Machine learning",
"level": 1,
"score": 45.0
},
{
"id": "https://openalex.org/C126255220",
"wikidata": "https://www.wikidata.org/wiki/Q141495",
"display_name": "Mathematical optimization",
"level": 1,
"score": 37.2
},
{
"id": "https://openalex.org/C11413529",
"wikidata": "https://www.wikidata.org/wiki/Q8366",
"display_name": "Algorithm",
"level": 1,
"score": 36.7
},
{
"id": "https://openalex.org/C154945302",
"wikidata": "https://www.wikidata.org/wiki/Q11660",
"display_name": "Artificial intelligence",
"level": 1,
"score": 36.2
},
{
"id": "https://openalex.org/C62520636",
"wikidata": "https://www.wikidata.org/wiki/Q944",
"display_name": "Quantum mechanics",
"level": 1,
"score": 32.6
},
{
"id": "https://openalex.org/C28826006",
"wikidata": "https://www.wikidata.org/wiki/Q33521",
"display_name": "Applied mathematics",
"level": 1,
"score": 26.6
},
{
"id": "https://openalex.org/C127313418",
"wikidata": "https://www.wikidata.org/wiki/Q1069",
"display_name": "Geology",
"level": 0,
"score": 26.1
},
{
"id": "https://openalex.org/C205649164",
"wikidata": "https://www.wikidata.org/wiki/Q1071",
"display_name": "Geography",
"level": 0,
"score": 25.2
},
{
"id": "https://openalex.org/C134306372",
"wikidata": "https://www.wikidata.org/wiki/Q7754",
"display_name": "Mathematical analysis",
"level": 1,
"score": 24.3
},
{
"id": "https://openalex.org/C162324750",
"wikidata": "https://www.wikidata.org/wiki/Q8134",
"display_name": "Economics",
"level": 0,
"score": 24.3
},
{
"id": "https://openalex.org/C86803240",
"wikidata": "https://www.wikidata.org/wiki/Q420",
"display_name": "Biology",
"level": 0,
"score": 21.1
},
{
"id": "https://openalex.org/C2524010",
"wikidata": "https://www.wikidata.org/wiki/Q8087",
"display_name": "Geometry",
"level": 1,
"score": 20.2
}
],
"counts_by_year": [
{
"year": 2024,
"works_count": 8,
"cited_by_count": 335
},
{
"year": 2023,
"works_count": 7,
"cited_by_count": 456
},
{
"year": 2022,
"works_count": 28,
"cited_by_count": 393
},
{
"year": 2021,
"works_count": 36,
"cited_by_count": 308
},
{
"year": 2020,
"works_count": 25,
"cited_by_count": 221
},
{
"year": 2019,
"works_count": 21,
"cited_by_count": 169
},
{
"year": 2018,
"works_count": 19,
"cited_by_count": 196
},
{
"year": 2017,
"works_count": 6,
"cited_by_count": 125
},
{
"year": 2016,
"works_count": 4,
"cited_by_count": 66
},
{
"year": 2015,
"works_count": 22,
"cited_by_count": 71
},
{
"year": 2014,
"works_count": 10,
"cited_by_count": 46
},
{
"year": 2013,
"works_count": 12,
"cited_by_count": 24
},
{
"year": 2012,
"works_count": 8,
"cited_by_count": 18
}
],
"works_api_url": "https://api.openalex.org/works?filter=author.id:A5084145417",
"updated_date": "2024-08-20T07:34:43.821992",
"created_date": "2023-07-21",
"_id": "https://openalex.org/A5084145417"
},
"ORCID": {
"@context": "http://schema.org",
"@type": "Person",
"@id": "https://orcid.org/0000-0002-3517-337X",
"mainEntityOfPage": "https://orcid.org/0000-0002-3517-337X",
"givenName": "John",
"familyName": "Jakeman",
"address": {
"addressCountry": "US",
"@type": "PostalAddress"
},
"alumniOf": [
{
"@type": "Organization",
"name": "Australian National University",
"alternateName": "Mathematics",
"identifier": {
"@type": "PropertyValue",
"propertyID": "RINGGOLD",
"value": "2219"
}
},
{
"@type": "Organization",
"name": "Australian National University",
"alternateName": "Mathematics",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ROR",
"value": "https://ror.org/019wvm592"
}
}
],
"affiliation": [
{
"@type": "Organization",
"name": "Sandia National Laboratories",
"alternateName": "Optimization and Uncertainty Quantification",
"identifier": {
"@type": "PropertyValue",
"propertyID": "RINGGOLD",
"value": "1105"
}
},
{
"@type": "Organization",
"name": "Sandia National Laboratories",
"alternateName": "Optimization and Uncertainty Quantification",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ROR",
"value": "https://ror.org/01apwpt12"
}
},
{
"@type": "Organization",
"name": "Sandia National Laboratories",
"alternateName": "Optimization and Uncertainty Quantificaiton",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ROR",
"value": "https://ror.org/01apwpt12"
}
},
{
"@type": "Organization",
"name": "Statistical and Applied Mathematical Sciences Institute",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ROR",
"value": "https://ror.org/01shctp43"
}
},
{
"@type": "Organization",
"name": "Purdue University System",
"alternateName": "Mathematics",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ROR",
"value": "https://ror.org/05p8z3f47"
}
}
],
"@reverse": {
"creator": [
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.5194/egusphere-2024-2209",
"name": "An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.5194/egusphere-2024-2209"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.18174/sesmo.18678",
"name": "Assessing convergence in global sensitivity analysis: a review of methods for assessing and monitoring convergence",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.18174/sesmo.18678"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.5194/egusphere-2024-1677",
"name": "Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.5194/egusphere-2024-1677"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1615/int.j.uncertaintyquantification.2023047605",
"name": "HYPERDIFFERENTIAL SENSITIVITY ANALYSIS IN THE CONTEXT OF BAYESIAN INFERENCE APPLIED TO ICE-SHEET PROBLEMS",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1615/int.j.uncertaintyquantification.2023047605"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.envsoft.2023.105825",
"name": "PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.envsoft.2023.105825"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.cma.2023.116205",
"name": "Multifidelity uncertainty quantification with models based on dissimilar parameters",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.cma.2023.116205"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1029/2022wr032194",
"name": "A Decision\u2010Relevant Factor\u2010Fixing Framework: Application to Uncertainty Analysis of a High\u2010Dimensional Water Quality Model",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1029/2022wr032194"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.2514/6.2023-0917",
"name": "Improving Bayesian networks multifidelity surrogate construction with basis adaptation",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.2514/6.2023-0917"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.2514/6.2023-1481",
"name": "Strategies for Automation of Model Tuning in Multi-fidelity Trajectory Uncertainty Propagation",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.2514/6.2023-1481"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1109/access.2023.3284837",
"name": "Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1109/access.2023.3284837"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1029/2021ms002831",
"name": "Global Sensitivity Analysis Using the Ultra\u2010Low Resolution Energy Exascale Earth System Model",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1029/2021ms002831"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1002/nme.6958",
"name": "Adaptive experimental design for multi\u2010fidelity surrogate modeling of multi\u2010disciplinary systems",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1002/nme.6958"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1137/20m1357615",
"name": "Risk-Adapted Optimal Experimental Design",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1137/20m1357615"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.ress.2021.108280",
"name": "Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.ress.2021.108280"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.envsoft.2021.105290",
"name": "Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.envsoft.2021.105290"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.2514/6.2022-1099",
"name": "Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.2514/6.2022-1099"
}
},
{
"@type": "CreativeWork",
"name": "Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning",
"identifier": {
"@type": "PropertyValue",
"propertyID": "uri",
"value": "http://jmlr.org/papers/v23/21-0225.html"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1007/s00466-021-02042-0",
"name": "MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1007/s00466-021-02042-0"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.4208/cicp.oa-2020-0060",
"name": "Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.4208/cicp.oa-2020-0060"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.envsoft.2020.104954",
"name": "The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.envsoft.2020.104954"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1137/20m1342859",
"name": "Data-Driven Learning of Nonautonomous Systems",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1137/20m1342859"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1615/int.j.uncertaintyquantification.2020034123",
"name": "DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1615/int.j.uncertaintyquantification.2020034123"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.addma.2020.101593",
"name": "Non-destructive simulation of node defects in additively manufactured lattice structures",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.addma.2020.101593"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1029/2020wr027721",
"name": "Modeling Water Quality in Watersheds: From Here to the Next Generation",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1029/2020wr027721"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.jcp.2020.109518",
"name": "Optimal experimental design for prediction based on push-forward probability measures",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.jcp.2020.109518"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.jcp.2020.109257",
"name": "A generalized approximate control variate framework for multifidelity uncertainty quantification",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.jcp.2020.109257"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1002/nme.6268",
"name": "Adaptive multi\u2010index collocation for uncertainty quantification and sensitivity analysis",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1002/nme.6268"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1615/jmachlearnmodelcomput.2020035155",
"name": "A SURVEY OF CONSTRAINED GAUSSIAN PROCESS REGRESSION: APPROACHES AND IMPLEMENTATION CHALLENGES",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1615/jmachlearnmodelcomput.2020035155"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1615/int.j.uncertaintyquantification.2020032978",
"name": "MFNets: MULTI-FIDELITY DATA-DRIVEN NETWORKS FOR BAYESIAN LEARNING AND PREDICTION",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1615/int.j.uncertaintyquantification.2020032978"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.envsoft.2019.07.007",
"name": "Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.envsoft.2019.07.007"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.cma.2019.03.049",
"name": "Polynomial chaos expansions for dependent random variables",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.cma.2019.03.049"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85064648323"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.2514/6.2019-0722",
"name": "Recent advancements in multilevel-multifidelity techniques for forward UQ in the DARPA sequoia project",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.2514/6.2019-0722"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85068997543"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.jcp.2018.08.010",
"name": "Gradient-based optimization for regression in the functional tensor-train format",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.jcp.2018.08.010"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85052290013"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.cma.2018.04.009",
"name": "Generation and application of multivariate polynomial quadrature rules",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85046963014"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.cma.2018.04.009"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1007/978-981-10-7811-8_15",
"name": "An Overview of Methods to Identify and Manage Uncertainty for Modelling Problems in the Water\u2013Environment\u2013Agriculture Cross-Sector",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1007/978-981-10-7811-8_15"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1137/16m1087229",
"name": "Combining push-forward measures and bayes' rule to construct consistent solutions to stochastic inverse problems",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1137/16m1087229"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85046818004"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1137/17m112590x",
"name": "Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1137/17m112590x"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85058213189"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1137/18m1181675",
"name": "Convergence of probability densities using approximate models for forward and inverse problems in uncertainty quantification",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1137/18m1181675"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85056128186"
}
]
},
{
"@type": "CreativeWork",
"name": "Multilevel-multifidelity approaches for forward uq in the DARPA SEQUOIA project",
"identifier": {
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85044335981"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1115/1.4037457",
"name": "Optimal experimental design using a consistent Bayesian approach",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85047052602"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1115/1.4037457"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1615/int.j.uncertaintyquantification.2018026902",
"name": "TIME AND FREQUENCY DOMAIN METHODS FOR BASIS SELECTION IN RANDOM LINEAR DYNAMICAL SYSTEMS",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1615/int.j.uncertaintyquantification.2018026902"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.2514/6.2017-1327",
"name": "Scalable Environment for Quantification of Uncertainty and Optimization in Industrial Applications (SEQUOIA)",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.2514/6.2017-1327"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1090/mcom/3192",
"name": "A christoffel function weighted least squares algorithm for collocation approximations",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85014451167"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1090/mcom/3192"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1137/16m1063885",
"name": "A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-85021842585"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1137/16m1063885"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.jcp.2015.02.025",
"name": "Enhancing \u21131-minimization estimates of polynomial chaos expansions using basis selection",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84924093639"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.jcp.2015.02.025"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.jcp.2014.09.014",
"name": "Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84907736357"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.jcp.2014.09.014"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1137/140970100",
"name": "Local polynomial chaos expansion for linear differential equations with high dimensional random inputs",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1137/140970100"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84923871286"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.2514/6.2015-0500",
"name": "Overview of selected DOE/NNSA predictive science initiatives: The predictive science academic alliance program and the DAKOTA project",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84980385771"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.2514/6.2015-0500"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1137/140966368",
"name": "Adaptive Leja sparse grid constructions for stochastic collocation and high-dimensional approximation",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1137/140966368"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84919625344"
}
]
},
{
"@type": "CreativeWork",
"name": "Practical identifiability analysis of environmental models",
"identifier": {
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84911927464"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1007/978-3-642-31703-3-9",
"name": "Local and dimension adaptive stochastic collocation for uncertainty quantification",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1007/978-3-642-31703-3-9"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84874430506"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.jcp.2013.02.035",
"name": "Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84875802025"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.jcp.2013.02.035"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1007/978-3-642-31703-3_9",
"name": "Local and Dimension Adaptive Stochastic Collocation for Uncertainty Quantification",
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1007/978-3-642-31703-3_9"
}
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.jcp.2011.02.022",
"name": "Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.jcp.2011.02.022"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-79952898773"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1007/s10236-010-0312-4",
"name": "Towards spatially distributed quantitative assessment of tsunami inundation models",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1007/s10236-010-0312-4"
},
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-77958103880"
}
]
},
{
"@type": "CreativeWork",
"@id": "https://doi.org/10.1016/j.jcp.2010.03.003",
"name": "Numerical approach for quantification of epistemic uncertainty",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-77951620321"
},
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.jcp.2010.03.003"
}
]
},
{
"@type": "CreativeWork",
"name": "Stochastic galerkin and collocation methods for quantifying uncertainty in differential equations: A review",
"identifier": {
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-84896692798"
}
},
{
"@type": "CreativeWork",
"name": "Inundation modelling of the December 2004 Indian ocean tsunami",
"identifier": {
"@type": "PropertyValue",
"propertyID": "eid",
"value": "2-s2.0-80052922543"
}
}
]
},
"url": "https://quantifying-uncertainty.com/",
"identifier": {
"@type": "PropertyValue",
"propertyID": "Scopus Author ID",
"value": "35758908600"
}
}
}
