arXiv subjects

Jiayuan Dong

Publications and source records attributed to Jiayuan Dong.

7 recordsLinked to original sources

arXiv2026

Matching Urban Flood Sensor Placement to Monitoring Objectives Using Bayesian Optimal Experimental Design

Flood-monitoring sensors are often placed according to coverage, access, or expected inundation. However, the value of a measurement depends on the prediction or decision it is intended to inform. Using tRIBS-Urban simulations and a neural-network surrogate of the August 2014 metropolitan Detroit flood, we examine how this learning target changes single-sensor placement. Across 2,576 candidate locations, we compare parameter-oriented optimal experimental design (PO-OED), which values expected information gain (EIG) about model parameters, with goal-oriented optimal experimental design (GO-OED), which values EIG about specified flood predictions. We also examine how parameter EIG evolves during the event, and illustrate that parameter learning translates unevenly into reductions in predictive uncertainty across locations and lead times. Under GO-OED, point-depth targets favor nearby locations, whereas regional-average and regional maximum-depth targets can favor nonlocal locations. Weighted multi-point objectives retain similar broad spatial patterns, although their computed max-EIG locations differ. Public geospatial data further provide illustrative feasibility and contextual classifications for deployment screening. These results show how monitoring objectives shape sensor placement in optimal experimental design, and motivates an objective-first workflow that defines the intended prediction and priorities, applies field-verified restrictions, and ranks locations by EIG.

stat.AP↗

arXiv2025

Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning

We present GO-CBED, a goal-oriented Bayesian framework for sequential causal experimental design. Unlike conventional approaches that select interventions aimed at inferring the full causal model, GO-CBED directly maximizes the expected information gain (EIG) on user-specified causal quantities of interest, enabling more targeted and efficient experimentation. The framework is both non-myopic, optimizing over entire intervention sequences, and goal-oriented, targeting only model aspects relevant to the causal query. To address the intractability of exact EIG computation, we introduce a variational lower bound estimator, optimized jointly through a transformer-based policy network and normalizing flow-based variational posteriors. The resulting policy enables real-time decision-making via an amortized network. We demonstrate that GO-CBED consistently outperforms existing baselines across various causal reasoning and discovery tasks-including synthetic structural causal models and semi-synthetic gene regulatory networks-particularly in settings with limited experimental budgets and complex causal mechanisms. Our results highlight the benefits of aligning experimental design objectives with specific research goals and of forward-looking sequential planning.

cs.LG↗

arXiv2025

Variational Bayesian Optimal Experimental Design with Normalizing Flows

Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte Carlo is computationally expensive and requires an explicit likelihood. 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. We introduce the use of normalizing flows (NFs) for representing variational distributions in vOED; we call this approach vOED-NFs. Specifically, we adopt NFs with a conditional invertible neural network architecture built from compositions of coupling layers, and enhanced with a summary network for data dimension reduction. We present Monte Carlo estimators to the lower bound along with gradient expressions to enable a gradient-based simultaneous optimization of the variational parameters and the design variables. The vOED-NFs algorithm is then validated in two benchmark problems, and demonstrated on a partial differential equation-governed application of cathodic electrophoretic deposition and an implicit likelihood case with stochastic modeling of aphid population. The findings suggest that a composition of 4--5 coupling layers is able to achieve lower EIG estimation bias, under a fixed budget of forward model runs, compared to previous approaches. The resulting NFs produce approximate posteriors that agree well with the true posteriors, able to capture non-Gaussian and multi-modal features effectively.

cs.LG↗

arXiv2024

Variational Sequential Optimal Experimental Design using Reinforcement Learning

We present variational sequential optimal experimental design (vsOED), a novel method for optimally designing a finite sequence of experiments within a Bayesian framework with information-theoretic criteria. vsOED employs a one-point reward formulation with variational posterior approximations, providing a provable lower bound to the expected information gain. Numerical methods are developed following an actor-critic reinforcement learning approach, including derivation and estimation of variational and policy gradients to optimize the design policy, and posterior approximation using Gaussian mixture models and normalizing flows. vsOED accommodates nuisance parameters, implicit likelihoods, and multiple candidate models, while supporting flexible design criteria that can target designs for model discrimination, parameter inference, goal-oriented prediction, and their weighted combinations. We demonstrate vsOED across various engineering and science applications, illustrating its superior sample efficiency compared to existing sequential experimental design algorithms.

stat.ML↗

arXiv2024

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

In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios. When generating our knowledge base for Retrieval-Augmented Generation (RAG), we observed that code-based models also perform exceptionally well in Chinese QA Pair Extraction task. Further, our experiments and the metrics we designed discovered that code-based models containing a certain amount of Chinese data achieve even better performance. Additionally, the capabilities of code-based English models in specified Chinese tasks offer a distinct perspective for discussion on the philosophical "Chinese Room" thought experiment.

cs.CL↗

arXiv2024

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

We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine learning enhancements. As a demonstrative application, we pursue the modeling of cathodic electrophoretic deposition (EPD), commonly known as e-coating. Our approach illustrates a systematic procedure for enhancing physical models by identifying their limitations through inference on experimental data and introducing adaptable model enhancements to address these shortcomings. We begin by tackling the issue of model parameter identifiability, which reveals aspects of the model that require improvement. To address generalizability , we introduce modifications which also enhance identifiability. However, these modifications do not fully capture essential experimental behaviors. To overcome this limitation, we incorporate interpretable yet flexible augmentations into the baseline model. These augmentations are parameterized by simple fully-connected neural networks (FNNs), and we leverage machine learning tools, particularly Neural Ordinary Differential Equations (Neural ODEs), to learn these augmentations. Our simulations demonstrate that the machine learning-augmented model more accurately captures observed behaviors and improves predictive accuracy. Nevertheless, we contend that while the model updates offer superior performance and capture the relevant physics, we can reduce off-line computational costs by eliminating certain dynamics without compromising accuracy or interpretability in downstream predictions of quantities of interest, particularly film thickness predictions. The entire process outlined here provides a structured approach to leverage data-driven methods. Firstly, it helps us comprehend the root causes of model inaccuracies, and secondly, it offers a principled method for enhancing model performance.

physics.comp-ph↗

arXiv2022

Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference

Bayesian inference allows the transparent communication of uncertainty in material flow analyses (MFAs), and a systematic update of uncertainty as new data become available. However, the method is undermined by the difficultly of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 U.S. steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods. The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' prior. A sensible, weakly-informative prior is also adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey (USGS) and the World Steel Association (WSA). The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre-assumed data noise levels, providing a more robust basis for decision-making that affects the system.

stat.AP↗