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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…
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks
Mohamed Aziz Bhouri, Michael Joly, Robert Yu +2
Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable…
3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems
Tryambak Gangopadhyay, Vikram Ramanan, Adedotun Akintayo +4
While analytical solutions of critical (phase) transitions in physical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical…