Vignesh Gopakumar

State-preserving constraints in Bayesian optimization are transformed into an efficient, anisotropic ellipsoid in design space, enabling robust optimization of complex engineering systems.

Physics-informed neural operators can now generalize to unseen physical regimes and extrapolate in time by explicitly encoding the underlying operator structure and decomposing PDEs into interpretable components.