publications

2025

1. JCP

   Sequential Infinite-Dimensional Bayesian Optimal Experimental Design with Derivative-Informed Latent Attention Neural Operator

   Jinwoo Go and Peng Chen

   Journal of Computational Physics, 2025

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   We propose a sequential Bayesian optimal experimental design framework for infinite-dimensional parameter spaces using a latent-variable attention-based neural operator (LANO) surrogate for time-dependent PDEs, enabling scalable and efficient design optimization.

2. CMAME

   Accurate, Scalable, and Efficient Bayesian Optimal Experimental Design with Derivative-Informed Neural Operators

   Jinwoo Go and Peng Chen

   Computer Methods in Applied Mechanics and Engineering, 2025

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   We improve the computational efficiency of Bayesian optimal experimental design using derivative-informed neural operators (DINO), achieving accurate and scalable design optimization for high-dimensional problems.

3. NeurIPS

   A Plug-and-Play Query Synthesis Active Learning Framework for Neural PDE Solvers

   Zhiyuan Wang, Jinwoo Go, Byung-Jun Yoon, and 2 more authors

   In Advances in Neural Information Processing Systems, 2025

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   We propose a plug-and-play active learning framework for neural PDE solvers that synthesizes informative queries to improve sample efficiency and generalization.

2023

1. Patent

   Methods for Confidence Assessment with Feature Importance in Data Driven Algorithms

   Jeffrey Miles, Paul Ryan Craddock, and Jinwoo Go

   2023

2022

1. UAI

   Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Sets

   Jinwoo Go and Tobin Isaac

   In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, 2022

   Oral Presentation — Top 5%

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   We propose a distributionally robust formulation of expected information gain for Bayesian experimental design using ambiguity sets, enabling risk-averse experiment selection under prior uncertainty.