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