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A comprehensive list of publications is available here.
Preprints
Preprints
Ishan Khurjekar, Indrashish Saha, Lori Graham-Brady, & Somdatta Goswami (2025). Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs. [ArXiv]
Dibyajyoti Nayak & Somdatta Gosami (2025). TI-DeepONet: Learnable Time Integration for Stable Long-Term Extrapolation. [ArXiv]
Gianluca Fabiani, Hannes Vandecasteele, Somdatta Goswami, Constantinos Siettos, and Ioannis G Kevrekidis (2025). Enabling Local Neural Operators to perform Equation-Free System-Level Analysis. [ArXiv]
Sharmila Karumuri, Lori Graham-Brady, & Somdatta Goswami (2025). Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems. [ArXiv]
Wei Wang, Maryam Hakimzadeh, Haihui Ruan, and Somdatta Goswami. Accelerating Multiscale Modeling with Hybrid Solvers: Coupling FEM and Neural Operators with Domain Decomposition. [ArXiv]
Somdatta Gosami, Dimitris G. Giovanis, Bowei Li, Seymour MJ Spence, & Michael D. Shields (2025). Neural Operators for Stochastic Modeling of Nonlinear Structural System Response to Natural Hazards. [ArXiv]
Vijay Kag, Dibakar Roy Sarkar, Birupaksha Pal, & Somdatta Goswami (2024). Learning Hidden Physics and System Parameters with Deep Operator Networks. [ArXiv]
Wei Wang, Tang Paai Wong, Haihui Ruan, & Somdatta Goswami (2024). Causality-Respecting Adaptive Refinement for PINNs: Enabling Precise Interface Evolution in Phase Field Modeling. [ArXiv]
K. Michałowska, Somdatta Goswami, G. E. Karniadakis, & S. Riemer-Sørensen (2023). DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks. [ArXiv]
A. Chakraborty, C. Anitescu, Somdatta Goswami, X. Zhuang, & T. Rabczuk (2022). Variational energy based XPINNs for phase field analysis in brittle fracture. [ArXiv]
Journal Papers
Nina Sheng Li, Adriana Coll De Peña, Matei Vaduva, Somdatta Goswami, & Anubhav Tripathi. Characterizing ssRNA and dsRNA electrophoretic behavior: empirical insights with neural network-aided predictions. Analyst (2025). [Journal]
Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis, & Michael D. Shields. A Resolution Independent Neural Operator. Computer Methods in Applied Mechanics and Engineering (2025). [Journal][ArXiv]
Anastasia S Georgiou, Arjun Manoj, Pei-Chun Su, Ronald R. Coifman, Ioannis G Kevrekidis, & Somdatta Goswami. From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics. Computers & Chemical Engineering (2025): 109201. [Journal][ChemRxiv][Code]
Sharmila Karumuri, Lori Graham-Brady, & Somdatta Goswami. Efficient Training of Deep Neural Operator Networks via Randomized Sampling. World Scientific Annual Review of Artificial Intelligence 3(2025): 2540001. [Journal][ArXiv][Code][Video]
Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D. Shields, & George Em Karniadakis. Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving. Neural Networks 184 (2025): 107113. [Journal][ArXiv][Code]
Tyler Ingebrand, Adam J. Thorpe, Somdatta Goswami, Krishna Kumar, & Ufuk Topcu. Basis-to-Basis Operator Learning Using Function Encoders. Computer Methods in Applied Mechanics and Engineering 435 (2025): 117646. [Journal][ArXiv][Code]
Luis Mandl, Somdatta Goswami, Lena Lambers, & Tim Ricken. Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning. Computer Methods in Applied Mechanics and Engineering 434 (2025): 117586. [Journal][ArXiv][Code]
K. Kontolati, Somdatta Goswami, G. E. Karniadakis, & M. D. Shields. Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems. Nature Communications 15.1 (2024): 5101. [Journal][ArXiv] [Code]
Q. Cao, Somdatta Goswami, & G. E. Karniadakis. Laplace Neural Operator for Solving Differential Equations. Nature Machine Intelligence (2024): 1-10. [Journal][ArXiv][Code]
N. Borrel-Jensen, Somdatta Goswami, A. P. Engsig-Karup, G. E. Karniadakis, & C. H. Jeong. Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators. Proceedings of the National Academy of Sciences 121 (2024), p.e2312159120. [Journal][ArXiv] [Code]
Somdatta Goswami, A. D. Jagtap, H. Babaee, B. T. Susi & G. E. Karniadakis. Learning stiff chemical kinetics using extended deep neural operators. Computer Methods in Applied Mechanics and Engineering 419 (2024), p.116674. [Journal][ArXiv] [Code]
Q. Cao, Somdatta Goswami, T. Tripura, S. Chakraborty, & G. E. Karniadakis. Deep neural operators can predict the real-time response of floating offshore structures under irregular waves, Computers & Structures 291 (2024), p.107228. [Journal] [ArXiv] [Code]
V. Kumar, Somdatta Goswami, D. J. Smith, & G. E. Karniadakis. Real-time prediction of gas flow dynamics in diesel engines using a deep neural operator framework. Applied Intelligence (2023),p. 1-21. [Journal][ArXiv]
M. L. Taccari, H. Wang, Somdatta Goswami, J. Nuttall, X. Chen, P. K. Jimack. Developing a cost-effective emulator for groundwater flow modeling using deep neural operators. Journal of Hydrology (2023), p.130551. [Journal][ArXiv] [Code]
A. Kahana, E. Zhang, Somdatta Goswami, G. E. Karniadakis, R. Ranade, & J. Pathak. On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations, Computational Mechanics (2023), p.1-14. [Journal] [ArXiv]
K. Kontolati, Somdatta Goswami, M. D. Shields, & G. E. Karniadakis. On the influence of over-parameterization in manifold based surrogates and deep neural operators, Journal of Computational Physics 479 (2023), p.112008. [Journal] [ArXiv] [Code]
Somdatta Goswami, K. Kontolati, M. D. Shields, & G. E. Karniadakis. Deep transfer learning for partial differential equations under conditional shift with DeepONet, Nature Machine Intelligence 4 (2022), p.1-10. [Journal] [ArXiv] [Code]
V. Oommen, K. Shukla, Somdatta Goswami, R. Dingreville, & G. E. Karniadakis. Learning two-phase microstructure evolution using neural operators and autoencoder architectures, npj Computational Materials 8 (2022), p.190. [Journal] [ArXiv] [Code]
Somdatta Goswami, D. S. Li, B. V. Rego, M. Latorre, J. D. Humphrey, & G. E. Karniadakis. Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms. Journal of the Royal Society Interface 19 (2022). [Journal] [ArXiv] [Code]
R. Bharali, Somdatta Goswami, C. Anitescu, & T. Rabczuk. A robust monolithic solver for phase-field fracture integrated with fracture energy based arc-length method and under-relaxation. Computer Methods in Applied Mechanics and Engineering 394 (2022), p.114587. [Journal] [ArXiv] [Code]
L. Lu, X. Meng, S. Cai, Z. Mao, Somdatta Goswami, Z. Zhang, & G. E. Karniadakis. A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data. Computer Methods in Applied Mechanics and Engineering 393 (2022), p.114778. [Journal] [ArXiv] [Code]
Somdatta Goswami, M. Yin, Y. Yu, & G. E. Karniadakis. A physics-informed variational DeepONet for predicting the crack path in brittle materials, Computer Methods in Applied Mechanics and Engineering 391 (2022), p.114587. [Journal] [ArXiv]
T. Chatterjee, S. Chakraborty, Somdatta Goswami, S. Adhikari, & M. I. Friswell. Robust topological designs for extreme metamaterial micro-structures, Sci Rep 11 (2021), p.15221. [Journal]
Somdatta Goswami, C. Anitescu, & T. Rabczuk. Adaptive fourth-order phase field analysis using deep energy minimization, Theoretical and Applied Fracture Mechanics 107 (2020), p.102527. [Journal] [Code]
Somdatta Goswami, C. Anitescu, & T. Rabczuk. Adaptive fourth-order phase field analysis for brittle fracture, Computer Methods in Applied Mechanics and Engineering 361 (2020), p.112808. [Journal] [Code]
E. Samaniego, C. Anitescu, Somdatta Goswami, V. M. Nguyen-Thanh, H. Guo, K. Hamdia, X. Zhuang, & T. Rabczuk. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications, Computer Methods in Applied Mechanics and Engineering 362 (2020), p.112790. [Journal] [ArXiv]
Somdatta Goswami, C. Anitescu, S. Chakraborty, & T. Rabczuk. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture, Theoretical and Applied Fracture Mechanics 106 (2019), p.102447. [Journal] [ArXiv] [Code]
Somdatta Goswami, C. Anitescu, & T. Rabczuk. Adaptive phase field analysis with dual hierarchical meshes for brittle fracture, Engineering Fracture Mechanics 218 (2019), p.106608. [Journal] [Code]
Somdatta Goswami, S. Chakraborty, R. Chowdhury, & T. Rabczuk. Threshold shift method for reliability-based design optimization, Structural and Multidisciplinary Optimization 60(5) (2019), p.2053–2072. [Journal]
S. Chakraborty, Somdatta Goswami, & T. Rabczuk. A surrogate assisted adaptive framework for robust topology optimization, Computer Methods in Applied Mechanics and Engineering 346 (2019), p.63-84. [Journal]
Somdatta Goswami, Shyamal Ghosh, and Subrata Chakraborty. Reliability analysis of structures by iterative improved response surface method, Structural Safety 60 (2016), p.56–66. [Journal]
Conference Proceedings
Conference Proceedings
Dibakar Roy Sarkar, Sukanta Basu, Lance Manuel, & Somdatta Goswami. Uncertainty-Aware Optimization in Engineered Systems via Gradient Boosting and Differential Evolution. Proceedings of the 35th European Safety and Reliability & the 33rd Society for Risk Analysis Europe Conference (2025). [Journal][Code][Video]
K. Michałowska, Somdatta Goswami, G. E. Karniadakis, & S. Riemer-Sørensen. Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks. 2024 International Joint Conference on Neural Networks:1-8, IEEE (2024). [Journal] [ArXiv]
R. Mukherjee, S. K. Meinia, Somdatta Goswami, & G. Negi. Objective Evaluation of Poor Veins Using Image Processing Technique: An Outcome Analysis. Scientific Session – Finding Solutions for Donor Problems. The 30th Regional Congress of the International Society of Blood Transfusion (2019):10-11.
Somdatta Goswami, & S. Chakraborty. Adaptive Response Surface Method Based Efficient Monte Carlo Simulation. Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management (2014):2043-2052.
Somdatta Goswami, S. Chakraborty, & S. Ghosh. Adaptive response surface method in structural response approximation under uncertainty. International Conference on Structural Engineering and Mechanics (2013):194-202.
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