4 papers
Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems
Yuanzhe Wang, Alexandre M. Tartakovsky
We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by partial differential equations (PD…
Discrete Diffusion for Complex and Congested Multi-Agent Path Finding with Sparse Social Attention
Yuanzhe Wang, Tian Zhi, Zihang Wei +8
Multi-Agent Path Finding (MAPF) is a coordination problem that requires computing globally consistent, collision-free trajectories from individual start positions to assigned goal…
Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data
Jice Zeng, Yuanzhe Wang, Alexandre M. Tartakovsky +1
We present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary n…
Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models
Yuanzhe Wang, Alexandre M. Tartakovsky
We propose an approximate Bayesian method for quantifying the total uncertainty in inverse PDE solutions obtained with machine learning surrogate models, including operator learnin…