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