Somdatta Goswami

According to our database, Somdatta Goswami authored at least 38 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Physics-Informed Time-Integrated DeepONet: Temporal Tangent Space Operator Learning for High-Accuracy Inference.

Luis Mandl, Dibyajyoti Nayak, Tim Ricken, Somdatta Goswami

CoRR, August, 2025

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs.

Ishan Khurjekar, Indrashish Saha, Lori Graham-Brady, Somdatta Goswami

CoRR, July, 2025

TI-DeepONet: Learnable Time Integration for Stable Long-Term Extrapolation.

Dibyajyoti Nayak, Somdatta Goswami

CoRR, May, 2025

Enabling Local Neural Operators to perform Equation-Free System-Level Analysis.

Gianluca Fabiani, Hannes Vandecasteele, Somdatta Goswami, Constantinos I. Siettos, Ioannis G. Kevrekidis

CoRR, May, 2025

Accelerating Multiscale Modeling with Hybrid Solvers: Coupling FEM and Neural Operators with Domain Decomposition.

Wei Wang, Maryam Hakimzadeh, Haihui Ruan, Somdatta Goswami

CoRR, April, 2025

Neural Operators for Stochastic Modeling of Nonlinear Structural System Response to Natural Hazards.

Somdatta Goswami, Dimitris G. Giovanis, Bowei Li, Seymour M. J. Spence, Michael D. Shields

CoRR, February, 2025

Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems.

Sharmila Karumuri, Lori Graham-Brady, Somdatta Goswami

CoRR, January, 2025

Efficient Training of Deep Neural Operator Networks via Randomized Sampling.

Sharmila Karumuri, Lori Graham-Brady, Somdatta Goswami

World Sci. Annu. Rev. Artif. Intell., 2025

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

Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis

Neural Networks, 2025

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

Katarzyna Michalowska, Somdatta Goswami, George Em Karniadakis, Signe Riemer-Sørensen

Neurocomputing, 2025

From clutter to clarity: Emergent neural operators via questionnaire metrics.

Anastasia S. Georgiou, Arjun Manoj, Pei-Chun Su, Ronald R. Coifman, Ioannis G. Kevrekidis, Somdatta Goswami

Comput. Chem. Eng., 2025

2024

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

Varun Kumar, Somdatta Goswami, Daniel Smith, George Em Karniadakis

Appl. Intell., January, 2024

Laplace neural operator for solving differential equations.

Qianying Cao, Somdatta Goswami, George Em Karniadakis

Nat. Mac. Intell., 2024

Learning Hidden Physics and System Parameters with Deep Operator Networks.

Vijay Kag, Dibakar Roy Sarkar, Birupaksha Pal, Somdatta Goswami

CoRR, 2024

Causality-Respecting Adaptive Refinement for PINNs: Enabling Precise Interface Evolution in Phase Field Modeling.

Wei Wang, Tang Paai Wong, Haihui Ruan, Somdatta Goswami

CoRR, 2024

Basis-to-Basis Operator Learning Using Function Encoders.

Tyler Ingebrand, Adam J. Thorpe, Somdatta Goswami, Krishna Kumar, Ufuk Topcu

CoRR, 2024

Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning.

Luis Mandl, Somdatta Goswami, Lena Lambers, Tim Ricken

CoRR, 2024

A Resolution Independent Neural Operator.

Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis, Michael D. Shields

CoRR, 2024

Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks.

Katarzyna Michalowska, Somdatta Goswami, George Em Karniadakis, Signe Riemer-Sørensen

Proceedings of the International Joint Conference on Neural Networks, 2024

2023

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

Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis

J. Comput. Phys., April, 2023

DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks.

Katarzyna Michalowska, Somdatta Goswami, George Em Karniadakis, Signe Riemer-Sørensen

CoRR, 2023

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

Nikolas Borrel-Jensen, Somdatta Goswami, Allan P. Engsig-Karup, George Em Karniadakis, Cheol-Ho Jeong

CoRR, 2023

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

Maria Luisa Taccari, He Wang, Somdatta Goswami, Jonathan Nuttall, Xiaohui Chen, Peter K. Jimack

CoRR, 2023

Learning in latent spaces improves the predictive accuracy of deep neural operators.

Katiana Kontolati, Somdatta Goswami, George Em Karniadakis, Michael D. Shields

CoRR, 2023

LNO: Laplace Neural Operator for Solving Differential Equations.

Qianying Cao, Somdatta Goswami, George Em Karniadakis

CoRR, 2023

Learning stiff chemical kinetics using extended deep neural operators.

Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee, Bryan T. Susi, George Em Karniadakis

CoRR, 2023

2022

Deep transfer operator learning for partial differential equations under conditional shift.

Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis

Nat. Mac. Intell., December, 2022

On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations.

Adar Kahana, Enrui Zhang, Somdatta Goswami, George Em Karniadakis, Rishikesh Ranade, Jay Pathak

CoRR, 2022

Physics-Informed Deep Neural Operator Networks.

Somdatta Goswami, Aniruddha Bora, Yue Yu, George Em Karniadakis

CoRR, 2022

Variational energy based XPINNs for phase field analysis in brittle fracture.

Ayan Chakraborty, Cosmin Anitescu, Somdatta Goswami, Xiaoying Zhuang, Timon Rabczuk

CoRR, 2022

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms.

Somdatta Goswami, David S. Li, Bruno V. Rego, Marcos Latorre, Jay D. Humphrey, George Em Karniadakis

CoRR, 2022

Deep transfer learning for partial differential equations under conditional shift with DeepONet.

Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis

CoRR, 2022

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

Vivek Oommen, Khemraj Shukla, Somdatta Goswami, Rémi Dingreville, George Em Karniadakis

CoRR, 2022

2021

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

Ritukesh Bharali, Somdatta Goswami, Cosmin Anitescu, Timon Rabczuk

CoRR, 2021

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

Somdatta Goswami, Minglang Yin, Yue Yu, George E. Karniadakis

CoRR, 2021

2019

An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications.

Esteban Samaniego, Cosmin Anitescu, Somdatta Goswami, Vien Minh Nguyen-Thanh, Hongwei Guo, Khader Hamdia, Timon Rabczuk, Xiaoying Zhuang

CoRR, 2019

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

Somdatta Goswami, Cosmin Anitescu, Souvik Chakraborty, Timon Rabczuk

CoRR, 2019

Threshold shift method for reliability-based design optimization.

Somdatta Goswami, Souvik Chakraborty, Rajib Chowdhury, Timon Rabczuk

CoRR, 2019