DBPnet combines Bayesian inference, a physics-informed loss, SLLM suspension modeling, and a damper-inspired embedding in a PINN to estimate wheel loads with lower RMSE and MaxError than baselines in simulations and experiments.
Adaptive Physics-Informed Neural Networks for Markov- Chain Monte Carlo,
2 Pith papers cite this work. Polarity classification is still indexing.
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A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
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DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation
DBPnet combines Bayesian inference, a physics-informed loss, SLLM suspension modeling, and a damper-inspired embedding in a PINN to estimate wheel loads with lower RMSE and MaxError than baselines in simulations and experiments.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.