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This paper introduces Response Renormalization, a backward-pass framework to improve training stability in Deep Equilibrium Models by addressing near-singular Jacobian issues, demonstrated across various multiphysics applications.
This paper studies when conservation laws can be certified in learned latent world models, proposing bounded horizons that guarantee how long rollouts stay on physical invariant level sets using measurable model defects.
This paper proposes PE-MHL, a Physics-Encoded Modular Hybrid Layer framework that incrementally refines a physics-based model with data-driven sub-models, providing theoretical convergence guarantees and outperforming monolithic networks on control benchmarks.
Researchers propose a Physics-Informed Machine Learning (PIML) framework that integrates hydrological constraints into an LSTM loss function to improve short-term flood forecasting, particularly in data-scarce regimes. A 'Trend Alignment' constraint enforcing consistency between precipitation and discharge trends improves Nash-Sutcliffe Efficiency and eliminates unphysical predictions during extreme events.
This paper presents a novel deep learning approach to predict inertial lift forces in microfluidic devices without explicit geometric parameters, enabling better generalization to unseen channel cross-sections compared to previous models.
This paper proposes an active learning framework to couple high-fidelity Modelica simulations with simpler surrogate models (SINDyC, FNN, GRU) for creating efficient digital twins of thermal energy distribution systems. The approach significantly reduces the number of simulation trajectories needed while maintaining predictive accuracy and enabling uncertainty quantification.