ISOMORPH is a modular digital twin simulator for supply chain networks that releases datasets exhibiting variance amplification and regime shifts for benchmarking forecasting models and performing forward uncertainty quantification.
Enforcing analytic constraints in neural networks emulating physical systems.Physical Review Letters, 126(9), March 2021
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SnareNet introduces a repair layer that navigates the range space of constraints plus adaptive relaxation training to enforce hard non-convex constraints on neural network outputs more reliably than prior methods.
A critical review of AI surrogate models for multiscale combustion that compares supervised, unsupervised, and physics-guided methods, identifies transferability and consistency challenges, and outlines future opportunities.
citing papers explorer
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ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
ISOMORPH is a modular digital twin simulator for supply chain networks that releases datasets exhibiting variance amplification and regime shifts for benchmarking forecasting models and performing forward uncertainty quantification.
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SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints
SnareNet introduces a repair layer that navigates the range space of constraints plus adaptive relaxation training to enforce hard non-convex constraints on neural network outputs more reliably than prior methods.
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AI-Powered Surrogate Modelling for Multiscale Combustion: A Critical Review and Opportunities
A critical review of AI surrogate models for multiscale combustion that compares supervised, unsupervised, and physics-guided methods, identifies transferability and consistency challenges, and outlines future opportunities.