2 citations · 2 across the 6 of their papers we have counts for
7 papers
From Sparse Sensors to Continuous Fields: STRIDE for Spatiotemporal Reconstruction
Yanjie Tong, Peng Chen
Reconstructing high-dimensional spatiotemporal fields from sparse point-sensor measurements is a central challenge in learning parametric PDE dynamics. Existing approaches often st…
Sequential Bayesian Optimal Experimental Design in Infinite Dimensions via Policy Gradient Reinforcement Learning
Kaichen Shen, Peng Chen
Sequential Bayesian optimal experimental design (SBOED) for PDE-governed inverse problems is computationally challenging, especially for infinite-dimensional random field parameter…
Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
Yuan Qiu, Wolfgang Dahmen, Peng Chen
Minimizing PDE-residual losses is a common strategy to promote physical consistency in neural operators. However, standard formulations often lack variational correctness, meaning…
PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure
Michael Buzzy, Andreas Robertson, Peng Chen +1
Recent advances in Foundation Models for Materials Science are poised to revolutionize the discovery, manufacture, and design of novel materials with tailored properties and respon…
PDPO: Parametric Density Path Optimization
Sebastian Gutierrez Hernandez, Peng Chen, Haomin Zhou
We introduce Parametric Density Path Optimization (PDPO), a novel method for computing action-minimizing paths between probability densities. The core idea is to represent the targ…
skscope: Fast Sparsity-Constrained Optimization in Python
Zezhi Wang, Jin Zhu, Peng Chen +5
Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad im…
Derivative-enhanced Deep Operator Network
Yuan Qiu, Nolan Bridges, Peng Chen
The deep operator networks (DeepONet), a class of neural operators that learn mappings between function spaces, have recently been developed as surrogate models for parametric part…