From the 1 of 9 linked papers with an AI index.
9 papers
MARUT: An Exascale-Ready, GPU-Accelerated High-Order CFD Framework with AMR for High-Speed Flows and Finite-Rate Chemistry
Trishit Mondal, Ameya D. Jagtap
MARUT is a GPU‑accelerated, high‑order computational fluid dynamics framework that uses spectral discontinuous Galerkin methods with adaptive mesh refinement to simulate compressib…
An Approximation Theory Perspective on Machine Learning
Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
A central problem in machine learning is often formulated as follows: Given a dataset , which is a sample drawn from an unknown probability distribution, th…
FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
Sidharth S. Menon, Ameya D. Jagtap
Kolmogorov-Arnold Networks (KANs) have recently emerged as a compelling alternative to multilayer perceptrons, offering enhanced interpretability via functional decomposition. Howe…
In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes
Trishit Mondal, Ameya D. Jagtap
Transformer architectures have revolutionized machine learning across a wide range of domains, from natural language processing to scientific computing. However, their growing depl…
Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty
Trishit Mondal, Ameya D. Jagtap
The hypersonic unstart phenomenon poses a major challenge to reliable air-breathing propulsion at Mach 5 and above, where strong shock-boundary-layer interactions and rapid pressur…
BubbleOKAN: A Physics-Informed Interpretable Neural Operator for High-Frequency Bubble Dynamics
Yunhao Zhang, Sidharth S. Menon, Lin Cheng +2
In this work, we employ physics-informed neural operators to map pressure profiles from an input function space to the corresponding bubble radius responses. Our approach employs a…