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Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
Sudeepta Mondal, Soumalya Sarkar
Most practical engineering design problems involve nonlinear spatio-temporal dynamical systems. Multi-physics simulations are often performed to capture the fine spatio-temporal sc…
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks
Shrenik Zinage, Sudeepta Mondal, Soumalya Sarkar
The need for scalable and expressive models in machine learning is paramount, particularly in applications requiring both structural depth and flexibility. Traditional deep learnin…
A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models
Agnimitra Sengupta, Sudeepta Mondal, Adway Das +1
Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these ap…