Variational Bayesian Optimal Experimental Design with Normalizing Flows

no code implementations • 8 Apr 2024 • Jiayuan Dong, Christian Jacobsen, Mehdi Khalloufi, Maryam Akram, Wanjiao Liu, Karthik Duraisamy, Xun Huan

Variational OED (vOED), in contrast, estimates a lower bound of the EIG without likelihood evaluations by approximating the posterior distributions with variational forms, and then tightens the bound by optimizing its variational parameters.

Dimensionality Reduction Experimental Design

Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition

no code implementations • 16 Jan 2024 • Christian Jacobsen, Jiayuan Dong, Mehdi Khalloufi, Xun Huan, Karthik Duraisamy, Maryam Akram, Wanjiao Liu

We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine learning enhancements.

Interpretable Machine Learning

Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task

no code implementations • 16 Jan 2024 • Linghan Zheng, Hui Liu, Xiaojun Lin, Jiayuan Dong, Yue Sheng, Gang Shi, Zhiwei Liu, Hongwei Chen

In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios.

Retrieval

Variational Sequential Optimal Experimental Design using Reinforcement Learning

no code implementations • 17 Jun 2023 • Wanggang Shen, Jiayuan Dong, Xun Huan

We introduce variational sequential Optimal Experimental Design (vsOED), a new method for optimally designing a finite sequence of experiments under a Bayesian framework and with information-gain utilities.

Experimental Design reinforcement-learning