SHRAV is an architecture-independent computational framework for physical modeling and inverse design, featuring a state-continuation core with declared reuse boundaries and explicit roles for learned evolution.
Proposes a physics-informed ML framework combined with multi-objective optimization for inverse design of Co-based superalloy compositions to enhance γ' solvus temperature while considering alloy density.
A browser-based acoustic simulator and inverse design tool for wind instrument bores without tone holes, using Transfer Matrix Method for forward modeling and adjoint-based Tikhonov regularization for inverse design optimization.
See LICENSE fileacoustic simulationwind instrument designinverse design
