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