Vivek Oommen

According to our database, Vivek Oommen authored at least 9 papers between 2022 and 2024.

Collaborative distances:

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

2024

Deep neural operators as accurate surrogates for shape optimization.

Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Nicholas Plewacki, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis

Eng. Appl. Artif. Intell., 2024

From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning.

Juan Diego Toscano, Vivek Oommen, Alan John Varghese, Zongren Zou, Nazanin Ahmadi Daryakenari, Chenxi Wu, George Em Karniadakis

CoRR, 2024

Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling.

Vivek Oommen, Aniruddha Bora, Zhen Zhang, George Em Karniadakis

CoRR, 2024

RiemannONets: Interpretable Neural Operators for Riemann Problems.

Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap, George Em Karniadakis

CoRR, 2024

2023

Rethinking materials simulations: Blending direct numerical simulations with neural operators.

Vivek Oommen, Khemraj Shukla, Saaketh Desai, Rémi Dingreville, George Em Karniadakis

CoRR, 2023

GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science.

Chenxi Wu, Alan John Varghese, Vivek Oommen, George Em Karniadakis

CoRR, 2023

Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils.

Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis

CoRR, 2023

2022

Solving Inverse Heat Transfer Problems Without Surrogate Models: A Fast, Data-Sparse, Physics Informed Neural Network Approach.

Vivek Oommen, Balaji Srinivasan

J. Comput. Inf. Sci. Eng., 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