Bayesian PINNs for elliptic PDEs have posteriors that contract around the true solution at near-optimal rates, with the prior adapting automatically to unknown smoothness.
On the estimation rate of bayesian pinn for inverse problems.arXiv preprint arXiv:2406.14808
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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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Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
Bayesian PINNs for elliptic PDEs have posteriors that contract around the true solution at near-optimal rates, with the prior adapting automatically to unknown smoothness.
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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.