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This paper presents PC-MCMC-CIGP, a gray-box workflow that combines spike-and-slab topology sampling with physical constraints and a Chemical-Informed Gaussian Process for reaction network discovery. The method demonstrates improved yield on styrene epoxidation and distinguishes elementary pathways from deceptive fits on a hydrogen-bromine benchmark.
Introduces energy conservation as a hard physical constraint on inter-module information flow in modular neural networks, enforcing exact preservation of activation energy at module boundaries to attenuate error propagation. Experiments on CIFAR-10 and a robotic pipeline show significant improvements in noise robustness.
This paper argues that generative AI for semiconductor manufacturing must enforce hard physical constraints by construction, not via post-hoc filtering, and surveys architectural approaches like physics-informed diffusion and neural-operator priors to achieve physics fidelity.
LaviGen is a framework that repurposes 3D generative models for autoregressive 3D layout generation, using an adapted 3D diffusion model with dual-guidance self-rollout distillation to achieve 19% higher physical plausibility and 65% faster computation than state-of-the-art methods on the LayoutVLM benchmark.