Threshold shift method for reliability-based design optimization. - Somdatta Goswami; Souvik Chakraborty; Rajib Chowdhury; Timon Rabczuk (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)

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)

A physics-informed variational DeepONet for predicting the crack path in brittle materials. - Somdatta Goswami; Minglang Yin; Yue Yu; George E. Karniadakis (CoRR, 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)

Learning two-phase microstructure evolution using neural operators and autoencoder architectures. - Vivek Oommen; Khemraj Shukla; Somdatta Goswami; Rémi Dingreville; 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)

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)

Variational energy based XPINNs for phase field analysis in brittle fracture. - Ayan Chakraborty 003; Cosmin Anitescu; Somdatta Goswami; Xiaoying Zhuang; Timon Rabczuk (CoRR, 2022)

Physics-Informed Deep Neural Operator Networks. - Somdatta Goswami; Aniruddha Bora; Yue Yu; George Em Karniadakis (CoRR, 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)

Deep transfer operator learning for partial differential equations under conditional shift. - Somdatta Goswami; Katiana Kontolati; Michael D. Shields; George Em Karniadakis (Nat. Mac. Intell., 2022)

Learning stiff chemical kinetics using extended deep neural operators. - Somdatta Goswami; Ameya D. Jagtap; Hessam Babaee; Bryan T. Susi; George Em Karniadakis (CoRR, 2023)

LNO: Laplace Neural Operator for Solving Differential Equations. - Qianying Cao; Somdatta Goswami; George Em Karniadakis (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)

Developing a cost-effective emulator for groundwater flow modeling using deep neural operators. - Maria Luisa Taccari; He Wang 002; Somdatta Goswami; Jonathan Nuttall; Xiaohui Chen; Peter K. Jimack (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)

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)

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., 2023)

Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks. - Katarzyna Michalowska; Somdatta Goswami; George Em Karniadakis; Signe Riemer-Sørensen (IJCNN, 2024)

A Resolution Independent Neural Operator. - Bahador Bahmani; Somdatta Goswami; Ioannis G. Kevrekidis; Michael D. Shields (CoRR, 2024)

Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning. - Luis Mandl; Somdatta Goswami; Lena Lambers; Tim Ricken (CoRR, 2024)

Basis-to-Basis Operator Learning Using Function Encoders. - Tyler Ingebrand; Adam J. Thorpe; Somdatta Goswami; Krishna Kumar; Ufuk Topcu (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)

Learning Hidden Physics and System Parameters with Deep Operator Networks. - Vijay Kag; Dibakar Roy Sarkar; Birupaksha Pal; Somdatta Goswami (CoRR, 2024)

Laplace neural operator for solving differential equations. - Qianying Cao; Somdatta Goswami; George Em Karniadakis (Nat. Mac. Intell., 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., 2024)

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)

Multi-resolution learning with DeepONets and long short-term memory neural networks. - Katarzyna Michalowska; Somdatta Goswami; George Em Karniadakis; Signe Riemer-Sørensen (Neurocomputing, 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)

Efficient Training of Deep Neural Operator Networks via Randomized Sampling. - Sharmila Karumuri; Lori Graham-Brady; Somdatta Goswami (World Sci. Annu. Rev. Artif. Intell., 2025)

Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems. - Sharmila Karumuri; Lori Graham-Brady; Somdatta Goswami (CoRR, 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, 2025)

Accelerating Multiscale Modeling with Hybrid Solvers: Coupling FEM and Neural Operators with Domain Decomposition. - Wei Wang; Maryam Hakimzadeh; Haihui Ruan; Somdatta Goswami (CoRR, 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, 2025)

TI-DeepONet: Learnable Time Integration for Stable Long-Term Extrapolation. - Dibyajyoti Nayak; Somdatta Goswami (CoRR, 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, 2025)

Physics-Informed Time-Integrated DeepONet: Temporal Tangent Space Operator Learning for High-Accuracy Inference. - Luis Mandl; Dibyajyoti Nayak; Tim Ricken; Somdatta Goswami (CoRR, 2025)