48 citations · 66 across the 7 of their papers we have counts for
3 papers · 1 filter
Development and Validation of a Physics-Guided Machine Learning Extrapolation Framework Using a Classical Transient Diffusion Benchmark
Ashutosh Yadav, Alok Dubey, Prodyut Ranjan Chakraborty +1
Machine learning models used in engineering are typically trained within limited operating ranges, yet reliable predictions are often required beyond these domains. Consequently, t…
DD-RNO: A Domain-Decomposed Routed Neural Operator for Airfoil Flow Prediction
T. A. Mehta, P. S. Bhati, H. D. Akolekar
Deep learning surrogates for RANS flow prediction around airfoils face two persistent bottlenecks. A single neural architecture cannot simultaneously resolve sharp near-wall bounda…
Realizability-Constrained Machine Learning for Turbulence Closures in Wake Flows
Talib Ansari, Priyank H. Mehta, Harshal D. Akolekar
Computational fluid dynamics (CFD)-driven machine learning frameworks based on symbolic regression offer a promising pathway for turbulence model discovery, but are often hindered…