| Markov chain Monte Carlo with neural network surrogates: Application to contaminant source identification Z Zhou, DM Tartakovsky Stochastic Environmental Research and Risk Assessment 35, 639-651, 2021 | 50 | 2021 |
| Thermal experiments for fractured rock characterization: theoretical analysis and inverse modeling Z Zhou, D Roubinet, DM Tartakovsky Water Resources Research 57 (12), e2021WR030608, 2021 | 18 | 2021 |
| Deep learning for simultaneous inference of hydraulic and transport properties Z Zhou, N Zabaras, DM Tartakovsky Water Resources Research 58 (10), e2021WR031438, 2022 | 8 | 2022 |
| Estimation of statistical properties of fracture networks from thermal-tracer experiments G Song, D Roubinet, Z Zhou, X Wang, DM Tartakovsky, X Song 47th Workshop on Geothermal Reservoir Engineering, 2022 | 1 | 2022 |
| Efficient inversion of synthetic thermal experiments for fractured rock characterization D Roubinet, Z Zhou, D Tartakovsky AGU Fall Meeting Abstracts 2021, H41C-06, 2021 | | 2021 |
| Deep Neural Network Surrogates for Inverse Problems Z Zhou Stanford University, 2021 | | 2021 |
| Numerical strategies for characterizing fractured rock from heat tracer experiments D Roubinet, Z Zhou, D Tartakovsky EGU General Assembly Conference Abstracts, 13699, 2020 | | 2020 |
Daniel M. TartakovskyStanford UniversityVerified email at stanford.edu
