Somdatta Goswami

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Assistant Professor (Research)

Brown University

United States

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Somdatta Goswami is an Assistant Professor (Research) at Brown University in the United States. His research focuses on the development of neural operators for stochastic modeling, uncertainty quantification, and efficient problem-solving techniques in physics-informed machine learning. Recent articles highlight advancements in AI-accelerated physics modeling and multi-resolution learning methodologies.

Recent Grants

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Ph.D.

Open Date: 2017-10-01

Close Date: 2021-09-01

Grant: Close

Masters in Technology

Open Date: 2011-07-01

Close Date: 2013-06-01

Somdatta Goswami

Research Interests

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Recent Grants

Articles (25)

Efficient Training of Deep Neural Operator Networks via Randomized Sampling

Neural operators for stochastic modeling of nonlinear structural system response to natural hazards

Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning

UQpy Version 4.2: Uncertainty quantification with Python

A resolution independent neural operator

Synergistic learning with multi-task DeepONet for efficient PDE problem solving

From clutter to clarity: Emergent neural operators via questionnaire metrics

Multi-resolution learning with DeepONets and long short-term memory neural networks

Time-marching neural operator–FE coupling: AI-accelerated physics modeling

Basis-to-basis operator learning using function encoders

Learning stiff chemical kinetics using extended deep neural operators

Laplace neural operator for solving differential equations

Developing a cost-effective emulator for groundwater flow modeling using deep neural operators

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

Real-time prediction of gas flow dynamics in diesel engines using a deep neural operator framework

On the influence of over-parameterization in manifold based surrogates and deep neural operators

Deep transfer operator learning for partial differential equations under conditional shift

Learning two-phase microstructure evolution using neural operators and autoencoder architectures

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

A robust monolithic solver for phase-field fracture integrated with fracture energy based arc-length method and under-relaxation

A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

Transfer learning enhanced physics informed neural network for phase-field modeling of fracture

Adaptive fourth-order phase field analysis for brittle fracture

Collaborators (16)

Allan Peter Engsig-Karup

Bahador Bahmani

Seymour M.J. Spence

zhongqiang zhang

Peter Jimack

Cheol-Ho Jeong

Michael D. Shields

Lori Graham-Brady

David Li

Anastasia Georgiou

He Wang

Khader Hamdia

Nikolas Borrel-Jensen

Tim Ricken

Bowei Li

Ameya Jagtap

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