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.
Investigates the relationship between residual scale and learnability in Δ-learning, demonstrating through molecular graph neural network evaluation on total energy targets that smaller residual targets do not necessarily yield better learning outcomes.
See LICENSE fileDelta learningresidual learningmolecular graph neural network
GravINNs embeds post-Newtonian equations of motion directly into neural network training to learn binary orbital dynamics, including single-orbit surrogates and extensions across a four-dimensional parameter space.
See LICENSE filegravity-informed neural networkspost-Newtonian approximationbinary dynamics
PosteriorBench introduces a benchmark for evaluating how well generative inverse solvers capture true posterior distributions in scientific inverse problems, addressing limitations of pointwise accuracy metrics.
See LICENSE fileinverse problemsgenerative modelsuncertainty quantification
A 36M-parameter neural network solver for partial differential equations based on U-Net architecture, enabling data-driven solving and prediction of PDE systems
