A-Z索引
Tartakovsky, Daniel M.
tartakovsky@stanford.edu; dtartako@stanford.edu
https://profiles.stanford.edu/daniel-tartakovsky（新しいタブで開く）
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/（新しいタブで開く）
Editorial Boards:
International Journal for Uncertainty Quantification（新しいタブで開く）
Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
Articles:
PROBABILISTIC PREDICTIONS OF INFILTRATION INTO HETEROGENEOUS MEDIA WITH UNCERTAIN HYDRAULIC PARAMETERS（新しいタブで開く） - Vol. 1 '2011（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
METHOD OF DISTRIBUTIONS FOR SYSTEMS WITH STOCHASTIC FORCING（新しいタブで開く） - Vol. 11 '2021（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
COMPUTING GREEN'S FUNCTIONS FOR FLOW IN HETEROGENEOUS COMPOSITE MEDIA（新しいタブで開く） - Vol. 3 '2013（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
PREFACE: FIRST QUEST CONFERENCE（新しいタブで開く） - Vol. 6 '2016（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS（新しいタブで開く） - Vol. 2 '2021（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
DYNAMIC MODE DECOMPOSITION FOR CONSTRUCTION OF REDUCED-ORDER MODELS OF HYPERBOLIC PROBLEMS WITH SHOCKS（新しいタブで開く） - Vol. 2 '2021（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN SUSTAINABILITY RESEARCH（新しいタブで開く） - Vol. 3 '2022（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
TRANSFER LEARNING ON MULTIFIDELITY DATA（新しいタブで開く） - Vol. 3 '2022（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
MACHINE-LEARNED INFERENCE OF FRACTURE FLOWRATE FROM TEMPERATURE LOGS（新しいタブで開く） - Vol. 5 '2024（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
ROLE OF PHYSICS IN PHYSICS-INFORMED MACHINE LEARNING（新しいタブで開く） - Vol. 5 '2024（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
AI-ENABLED CARDIOVASCULAR MODELS TRAINED ON MULTIFIDELITY SIMULATIONS DATA（新しいタブで開く） - Vol. 6 '2025（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
DOMAIN DECOMPOSITION FOR ENHANCEMENT OF REDUCED-ORDER MODELS（新しいタブで開く） - Vol. 6 '2025（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
TRANSFER LEARNING ON MULTI-DIMENSIONAL DATA: A NOVEL APPROACH TO NEURAL NETWORK-BASED SURROGATE MODELING（新しいタブで開く） - Vol. 6 '2025（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
FOURIER NEURAL OPERATOR SURROGATE OF LITHIUM-ION BATTERY MODELS（新しいタブで開く） - Vol. 7 '2026（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
GAUSSIAN-PROCESS MODELS OF POPULATION DYNAMICS（新しいタブで開く） - Vol. 7 '2026（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
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