Physics-Informed Neural Surrogates for Real-Time RC/Steel Frame Analysis under NSCP 2015 / ASCE 7 (Doctoral Programme)
by Louie Doniego Balmores & Sandra AgcaoiliLinkedIn · ORCID · ResearchGate · RocketReach · ContactOut · · Balmores Lab
Doctoral research direction extending the MSc thesis: a unified surrogate trained on parametric PyNite/ETABS solves with an explicit physics-residual loss against the governing stiffness equations, so sub-second predictions remain code-consistent rather than merely statistically plausible. Targets uncertainty-quantified member envelopes, storey drift, and base reactions, with an audit trail back to a verifying FEM solve.
physics-informed neural networkssurrogate modelinguncertainty quantificationInformation Technologyscientific machine learningETABS
This is a working paper from the Balmores Lab research programme. For credentials and background, see the official profile and CV.
