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Vivek Oommen
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Learning two-phase microstructure evolution using neural operators and autoencoder architectures
V Oommen, K Shukla, S Goswami, R Dingreville, GE Karniadakis
npj Computational Materials 8 (1), 190, 2022
572022
Solving inverse heat transfer problems without surrogate models: a fast, data-sparse, physics informed neural network approach
V Oommen, B Srinivasan
Journal of Computing and Information Science in Engineering 22 (4), 041012, 2022
292022
Deep neural operators as accurate surrogates for shape optimization
K Shukla, V Oommen, A Peyvan, M Penwarden, N Plewacki, L Bravo, ...
Engineering Applications of Artificial Intelligence 129, 107615, 2024
20*2024
RiemannONets: Interpretable Neural Operators for Riemann Problems
A Peyvan, V Oommen, AD Jagtap, GE Karniadakis
arXiv preprint arXiv:2401.08886, 2024
2024
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)
O Ovadia, V Oommen, A Kahana, A Peyvan, E Turkel, GE Karniadakis
arXiv preprint arXiv:2307.09072, 2023
2023
Rethinking materials simulations: Blending direct numerical simulations with neural operators
V Oommen, K Shukla, S Desai, R Dingreville, GE Karniadakis
arXiv preprint arXiv:2312.05410, 2023
2023
GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science
C Wu, AJ Varghese, V Oommen, GE Karniadakis
arXiv preprint arXiv:2312.03769, 2023
2023
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Articles 1–7