6 papers
GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model
Trishit Mondal, Prajwal Bharadwaj, Nikhil Karanjgaokar +1
Internal voids are ubiquitous defects in manufactured structures, yet their characterization remains challenging because their geometry is hidden and can only be inferred indirectl…
Adaptive hybrid coupling with operator inference, the overlapping Schwarz alternating method and reinforcement learning
Trishit Mondal, Irina Tezaur, Anthony Gruber
Hybrid domain decomposition methods provide a flexible framework for coupling full order models (FOMs) and reduced order models (ROMs), but typically assume the model assigned to e…
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
We present MARUT, a scalable multi-GPU computational fluid dynamics (CFD) framework designed for high-fidelity simulations of compressible flows spanning subsonic to hypersonic reg…
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…
Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning
Trishit Mondal, Ricardo Vinuesa, Ameya D. Jagtap
Active flow control of compressible transonic shock-boundary layer interactions over a two-dimensional RAE2822 airfoil at Re = 50,000 is investigated using deep reinforcement learn…