CWAEs use triangular decoders and latent independence in Wasserstein autoencoders to perform conditional simulation by capturing low-dimensional structure in conditioned and conditioning variables.
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Develops verifiable error bounds for PINN solutions of Lyapunov and HJB PDEs that turn residual bounds into relative error bounds, certified value function bounds, and valid Lyapunov functions.
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Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
CWAEs use triangular decoders and latent independence in Wasserstein autoencoders to perform conditional simulation by capturing low-dimensional structure in conditioned and conditioning variables.
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Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
Develops verifiable error bounds for PINN solutions of Lyapunov and HJB PDEs that turn residual bounds into relative error bounds, certified value function bounds, and valid Lyapunov functions.