Sourav Dutta
I am currently a Research Associate in the Computational Hydraulics Group at the Oden Institute for Computational Engineering & Sciences of the University of Texas at Austin, working with Prof. Clint Dawson. Previously, I was an ORISE postdoctoral fellow at the Coastal & Hydraulics Laboratory of the U.S. Army Engineer Research and Development Center, where I worked with Dr. Matthew Farthing. I received my Ph.D. in Mathematics from Texas A&M University in 2017, supervised by Prof. Prabir Daripa.
My research interests lie at the intersection of classical, physics-based computational methods and modern data-driven, machine learning-based techniques with applications to computational science and engineering. I am particularly interested in exploring ways to develop efficient and robust numerical approximations of real-world, large scale environmental flow problems by combining physical principles with modern machine learning algorithms, either by infusing physics-based regularization in the learning trajectory or by modeling the underlying differential operator.
- Scientific Machine Learning
- Model Order Reduction
- Computational Hydrology
- Uncertainty Quantification
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Ph.D. in Mathematics, 2017
Texas A&M University
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B.Sc. & M.Sc. in Mathematics & Computing, 2010
Indian Institute of Technology Kharagpur
Work Experience
Oden Institute for Computational Engineering & Sciences, UT Austin
Research Associate
Research Fellow
U.S. Army Engineer Research & Development Center (ERDC)
ORISE Postdoctoral Fellow
News
| Sep 14, 2026 | Our paper “Operator learning for predicting bulk wave parameters of spectral wave models” has been published in Ocean Engineering. It explores the use of DeepONets to predict bulk wave parameters such as significant wave height and gradient of radiation stresses in both benchmark and realistic experiments, as obtained from steady state simulations using a spectral wave model, SWAN. |
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| Aug 01, 2026 | Delighted to announce a new 3-year award from the National Science Foundation’s AI and Geosciences program, for the project “Collaborative Research: CAIG: Improving the understanding of coastal groundwater and ocean exchange using machine learning.” The project is led by Dr. Jonghyun Harry Lee (University of Hawai’i at Mānoa) as PI, and I will serve as co-PI leading the UT Austin team on developing neural-operator-based surrogate models for coastal groundwater–ocean exchange, including seawater intrusion and submarine groundwater discharge. Award runs September 2026 through August 2029. |
| Jun 01, 2026 | Our paper “A neural operator emulator for coastal and riverine shallow water dynamics” has been published in the Journal of Geophysical Research:Machine Learning and Computation. It introduces MITONet, a neural operator emulator framework for modeling shallow water flows in realistic coastal and riverine environments. |
| May 01, 2026 | Attending the HydroML 2026 Symposium during May 19-21, 2026, at the Oden Institute, and presenting our work on developing MITONet, a neural operator emulator for shallow water flows as well as related DeepONet-based surrogate models for spectral wave models. Also, attending USNC-TAM26, the 20th U.S. National Congress on Theoretical and Applied Mechanics, in Pasadena California during June 21-25, 2026, and presenting some preliminary results of an ongoing study on developing neural operator emulators for salt water intrusion in coastal aquifers. |
| Oct 01, 2022 | I have started my new position as Research Fellow at the Oden Institute for Computational Engineering & Sciences of The University of Texas at Austin. I will be working with Prof. Clint Dawson and other esteemed members of the Computational Hydraulics Group. Looking forward to an exciting and productive time ahead. |
