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Guoquan Wu

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

A Multi-Objective Optimization Framework for Adaptive Weighting in Physics-Informed Machine Learning

Guoquan Wu
Zhe Wu

Training physics-informed neural networks (PINNs) can be viewed as a multi-task optimization problem, where data-driven and physics-driven loss functions must be simultaneously minimized, despite the potential competition between them. Manually tuning the weight coefficients for various loss terms in PINNs is often time-consuming and lacks a systematic approach. To address this challenge, this work proposes an adaptive loss balancing framework for PINNs, using multi-objective optimization (MOO) algorithms to dynamically balance competing loss terms during training. Specifically, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is integrated into the PINN training process to explore the Pareto front of the multiple objectives. A novel variance-aware relative improvement (VARI) weighting method is proposed to translate Pareto-optimal information into adaptive loss weights. The proposed MOO-VARI method is validated through several examples, where the results show that the MOO-VARI PINN consistently outperforms standard PINN and other state-of-the-art adaptive weighting strategies in terms of convergence speed, predictive accuracy, and parameter estimation performance.

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AAAI Conference 2025 Conference Paper

An LLM-Empowered Adaptive Evolutionary Algorithm for Multi-Component Deep Learning Systems

Haoxiang Tian
Xingshuo Han
Guoquan Wu
An Guo
Yuan Zhou
Jie Zhang
Shuo Li
Jun Wei

Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes the first LLM-empowered adaptive evolutionary search algorithm to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), our approach promotes the LLM to comprehend the optimization problem and generate an initial population tailed to evolutionary objectives. Subsequently, it employs adaptive selection and variation to iteratively produce offspring, balancing the evolutionary efficiency and diversity. During the evolutionary process, to navigate away from the local optima, our approach integrates the evolutionary experience back into the LLM. This utilization harnesses the LLM's quantitative reasoning prowess to generate differential seeds, breaking away from current optimal solutions. We evaluate our approach in finding safety violations of MCDL systems, and compare its performance with state-of-the-art MOEA methods. Experimental results show that our approach can significantly improve the efficiency and diversity of the evolutionary search.

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ICRA Conference 2025 Conference Paper

HeRo: A State Machine-Based, Fault-Tolerant Framework for Heterogeneous Multi-Robot Collaboration

Ruijie Tang
Guoquan Wu
Tao Wang 0074
Wei Chen 0018
Jun Wei 0001

Heterogeneous robots can work together to accomplish a variety of complex tasks and have shown great potential in many fields. There are many efforts to make robot task orchestration more efficient. However, current methods still have some limitations, including the lack of a high-level abstraction for programming method and fault handling mechanism. In this paper, we design a state machine-based, fault-tolerant framework for heterogeneous multi-robot collaboration named HeRo, to effectively support the development of heterogeneous multi-robot systems. HeRo has three key techniques: (1) a state machine-based programming language to flexibly model robot behaviors and tasks; (2) a state synchronization mechanism to achieve information exchange and maintain the consistency among heterogeneous robots in distributed environments; (3) a fault detection and recovery mechanism to monitor the system's runtime states and use Large Language Model (LLM) combined with Planning Domain Definition Language (PDDL) to enable automated recovery. We evaluate the effectiveness and fault recovery capability of the framework by setting up manufacturing task and fault scenarios with varying difficulty in the ARIAC simulation environment, achieving a 100% task completion rate, with low system overhead and flexible scalability.

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