A-Z 索引
Tartakovsky, Daniel M.
tartakovsky@stanford.edu; dtartako@stanford.edu
https://profiles.stanford.edu/daniel-tartakovsky（在新标签页中打开）
Department of Energy Science & Engineering, Engineering, Stanford University, 367 Panama St., Stanford, CA 94305, USA
Energy Resources Engineering
367 Panama Street , Stanford, California, UNITED STATES, 94305
https://www.stanford.edu/（在新标签页中打开）
Editorial Boards:
International Journal for Uncertainty Quantification（在新标签页中打开）
Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
Articles:
PROBABILISTIC PREDICTIONS OF INFILTRATION INTO HETEROGENEOUS MEDIA WITH UNCERTAIN HYDRAULIC PARAMETERS（在新标签页中打开） - Vol. 1 '2011（在新标签页中打开） - International Journal for Uncertainty Quantification（在新标签页中打开）
METHOD OF DISTRIBUTIONS FOR SYSTEMS WITH STOCHASTIC FORCING（在新标签页中打开） - Vol. 11 '2021（在新标签页中打开） - International Journal for Uncertainty Quantification（在新标签页中打开）
COMPUTING GREEN'S FUNCTIONS FOR FLOW IN HETEROGENEOUS COMPOSITE MEDIA（在新标签页中打开） - Vol. 3 '2013（在新标签页中打开） - International Journal for Uncertainty Quantification（在新标签页中打开）
PREFACE: FIRST QUEST CONFERENCE（在新标签页中打开） - Vol. 6 '2016（在新标签页中打开） - International Journal for Uncertainty Quantification（在新标签页中打开）
DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS（在新标签页中打开） - Vol. 2 '2021（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
DYNAMIC MODE DECOMPOSITION FOR CONSTRUCTION OF REDUCED-ORDER MODELS OF HYPERBOLIC PROBLEMS WITH SHOCKS（在新标签页中打开） - Vol. 2 '2021（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN SUSTAINABILITY RESEARCH（在新标签页中打开） - Vol. 3 '2022（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
TRANSFER LEARNING ON MULTIFIDELITY DATA（在新标签页中打开） - Vol. 3 '2022（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
MACHINE-LEARNED INFERENCE OF FRACTURE FLOWRATE FROM TEMPERATURE LOGS（在新标签页中打开） - Vol. 5 '2024（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
ROLE OF PHYSICS IN PHYSICS-INFORMED MACHINE LEARNING（在新标签页中打开） - Vol. 5 '2024（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
AI-ENABLED CARDIOVASCULAR MODELS TRAINED ON MULTIFIDELITY SIMULATIONS DATA（在新标签页中打开） - Vol. 6 '2025（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
DOMAIN DECOMPOSITION FOR ENHANCEMENT OF REDUCED-ORDER MODELS（在新标签页中打开） - Vol. 6 '2025（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
TRANSFER LEARNING ON MULTI-DIMENSIONAL DATA: A NOVEL APPROACH TO NEURAL NETWORK-BASED SURROGATE MODELING（在新标签页中打开） - Vol. 6 '2025（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
FOURIER NEURAL OPERATOR SURROGATE OF LITHIUM-ION BATTERY MODELS（在新标签页中打开） - Vol. 7 '2026（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
GAUSSIAN-PROCESS MODELS OF POPULATION DYNAMICS（在新标签页中打开） - Vol. 7 '2026（在新标签页中打开） - Journal of Machine Learning for Modeling and Computing（在新标签页中打开）
Yang, Xiaoyu
FOURIER NEURAL OPERATOR SURROGATE OF LITHIUM-ION BATTERY MODELS
MACHINE-LEARNED INFERENCE OF FRACTURE FLOWRATE FROM TEMPERATURE LOGS
Abylkhani, B.
DOMAIN DECOMPOSITION FOR ENHANCEMENT OF REDUCED-ORDER MODELS
Wang, Peng
PREFACE: FIRST QUEST CONFERENCE
Zheng, Liange
DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS
Wainwright, Haruko Murakami
DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS
Viswanathan, Aditya
FOURIER NEURAL OPERATOR SURROGATE OF LITHIUM-ION BATTERY MODELS
Propp, Adrienne M.
TRANSFER LEARNING ON MULTI-DIMENSIONAL DATA: A NOVEL APPROACH TO NEURAL NETWORK-BASED SURROGATE MODELING
Chiofalo, Alessia
AI-ENABLED CARDIOVASCULAR MODELS TRAINED ON MULTIFIDELITY SIMULATIONS DATA
Barajas-Solano, David A.
COMPUTING GREEN'S FUNCTIONS FOR FLOW IN HETEROGENEOUS COMPOSITE MEDIA
Song, Dong H.
TRANSFER LEARNING ON MULTIFIDELITY DATA
Li, L. Gary
GAUSSIAN-PROCESS MODELS OF POPULATION DYNAMICS
Rutjens, Rik J. L.
METHOD OF DISTRIBUTIONS FOR SYSTEMS WITH STOCHASTIC FORCING
Chandra, Abhishek
ROLE OF PHYSICS IN PHYSICS-INFORMED MACHINE LEARNING
Ciriello, Valentina
AI-ENABLED CARDIOVASCULAR MODELS TRAINED ON MULTIFIDELITY SIMULATIONS DATA
MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN SUSTAINABILITY RESEARCH
Wang, Peng
PROBABILISTIC PREDICTIONS OF INFILTRATION INTO HETEROGENEOUS MEDIA WITH UNCERTAIN HYDRAULIC PARAMETERS
Xiu, Dongbin
COMPUTATIONAL FRAMEWORK FOR REAL-TIME DIGITAL TWINS
PREFACE: FIRST QUEST CONFERENCE
Lu, Hannah
DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS
DYNAMIC MODE DECOMPOSITION FOR CONSTRUCTION OF REDUCED-ORDER MODELS OF HYPERBOLIC PROBLEMS WITH SHOCKS
Jacobs, Gustaaf B.
METHOD OF DISTRIBUTIONS FOR SYSTEMS WITH STOCHASTIC FORCING
Careddu, L.
AI-ENABLED CARDIOVASCULAR MODELS TRAINED ON MULTIFIDELITY SIMULATIONS DATA
Bakarji, Joseph
ROLE OF PHYSICS IN PHYSICS-INFORMED MACHINE LEARNING
Dwivedi, Dipankar
DOMAIN DECOMPOSITION FOR ENHANCEMENT OF REDUCED-ORDER MODELS
Yabusaki, S. B.
DOMAIN DECOMPOSITION FOR ENHANCEMENT OF REDUCED-ORDER MODELS
Xiu, Isaac
GAUSSIAN-PROCESS MODELS OF POPULATION DYNAMICS
Ermakova, Dinara
DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS
Horne, Roland N.
MACHINE-LEARNED INFERENCE OF FRACTURE FLOWRATE FROM TEMPERATURE LOGS