About

Wei Chen is a Professor of Mechanical Engineering at Northwestern University and holds the Wilson-Cook Professorship in Engineering Design. She is also a Professor of Industrial Engineering and Management Sciences and Materials Science and Engineering by courtesy. Dr. Chen is the Director of the Integrated Design Automation Laboratory (IDEAL) and the Founding Director of the Predictive Science and Engineering Design (PSED) Cluster. Her research focuses on simulation-based design under uncertainty, artificial intelligence and machine learning for predictive science and engineering design, multifidelity modeling, uncertainty quantification, and data-driven design of heterogenous nano- and microstructural materials and programmable metamaterials. She is actively involved in developing digital twins for autonomous manufacturing, topology optimization, co-design of materials and structures, multidisciplinary design optimization, and network analysis for modeling consumer preferences and decision-based design. Her work integrates statistical inference, machine learning, and uncertainty quantification techniques to enable adaptive discovery and design of emerging materials systems, bridging disciplines such as materials science, manufacturing, structural mechanics, data science, and design optimization. Dr. Chen's research has been incorporated into commercial software and has made societal impacts through industrial collaborations, advancing multifunctional, lightweight, energy-efficient, and sustainable materials, products, and processes.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Mathematics
• Algorithm
• Machine Learning
• Materials science
• Nanotechnology
• Mechanical engineering
• Theoretical computer science
• Organic chemistry
• Physics
• Optics
• Electronic engineering
• Chemistry
• Biochemical engineering
• Mathematical optimization

Selected publications

• Intelligent Train Timetable Generation Technology Based on Monte Carlo Tree Search Algorithm

  PROMET - Traffic&Transportation · 2026-01-29

  articleOpen accessSenior author

  This paper presents an innovative approach to train timetable generation using Monte Carlo tree search (MCTS) integrated with a deep reinforcement learning technique. The generation and adjustment of train timetables for high-speed railways represent a complex optimisation problem with numerous rule-based constraints that traditional mathematical methods struggle to solve efficiently. Therefore, the train timetable generation problem is modelled as a discrete spatiotemporal Markov decision process, and a comprehensive MCTS-based algorithm is developed to effectively balance exploration and exploitation through a structured tree search mechanism. The result of the comparative analysis demonstrates that MCTS-based algorithms significantly outperform state-of-the-art reinforcement learning algorithms, including double deep Q-network (DDQN) and proximal policy optimisation (PPO), achieving optimal solutions 6.5 times faster with superior training stability. To validate the scalability and real-world applicability, a large-scale case study involving 120 pairs of trains on the Beijing-Shanghai High-Speed Rail corridor over an 18-hour period successfully resolved all 45,600 initial conflicts. The optimised timetables yield significant operational improvements, including a 16.4% reduction in average delay time, 22.8% improvement in track utilisation efficiency and 9.7% reduction in energy consumption. This research contributes to the advancement of intelligent railway operations optimisation and demonstrates the potential of MCTS-based approaches to transform complex transportation problems.

  PublisherOA PDFDOI

• Adaptive digital twin of sheet metal forming via proper orthogonal decomposition-based Koopman operator with model predictive control

  Journal of Manufacturing Systems · 2026-04-09

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Reinforcement learning-based control co-design of digital twin-enabled full-vehicle active suspension systems

  Structural and Multidisciplinary Optimization · 2026-04-01

  articleOpen accessSenior authorCorresponding

  Active suspension systems are critical for enhancing vehicle comfort, safety, and stability, yet their performance is often limited by fixed hardware designs and control strategies that cannot adapt to uncertain and dynamic operating conditions. Recent advances in Digital Twins (DTs) and Reinforcement Learning (RL) offer new opportunities for real-time, data-driven optimization across a vehicle's lifecycle. However, integrating these technologies into a unified framework for co-optimizing physical and control systems remains an open challenge. This work presents an RL-based Control Co-Design (CCD) framework for full-vehicle active suspensions using multi-generation design and DT concepts. Through integrating automatic differentiation into Deep Reinforcement Learning (DRL), we jointly optimize physical components of suspension systems and control policies under varying driver behaviors and environmental uncertainties. The DRL technique also addresses the challenge of partial observability, where only limited states can be sensed and fed back to the controller, by learning optimal control actions directly from available sensor information. The framework incorporates model updating with quantile learning to quantify data uncertainty, enabling real-time decision-making and adaptive learning from digital-physical interactions. The approach demonstrates personalized optimization of autonomous suspension systems under two distinct driving settings (mild and aggressive). The results show that the optimized systems achieve smoother trajectories and reduce control efforts by approximately 58% and 12% for mild and aggressive while improving ride comfort by approximately 17% and 28%, respectively. Contributions of this work include: (1) developing a DT-enabled CCD framework integrating DRL and uncertainty-aware model updating for full-vehicle active suspensions, (2) introducing a multi-generation design framework for self-improving systems across the whole lifecycle, and (3) demonstrating personalized optimization of active suspension systems for distinct types of drivers.

  PublisherOA PDFDOI

• Martensite Start Temperature Modeling via Artificial Neural Network Model

  steel research international · 2025-05-26

  article

  The martensite start temperature (M s ) of steels plays an important role in the formulation of heat‐treatment processes. Therefore, it is of great practical importance to predict M s accurately and rapidly. In the present work, machine learning (ML) methods are used to model M s based on the M s data of 1177 steels. Moreover, its generalization performance is verified using fivefold cross validation. Three different back‐propagation (BP) neural network algorithms (genetic algorithm [GA], particle swarm optimization, mind evolutionary algorithm) are used for optimal model selection. The results indicate that, among the three BP neural network algorithms, the GA–BP model has the highest prediction accuracy on the test set. The performances of GA–BP, Thermal–Calc, and JMatPro in predicting the M s of medium‐ and low‐carbon steels and high‐carbon steels are analyzed using an unknown dataset. The results show that the GA–BP model has strong generalization ability and can predict M s relatively accurately. The influence of alloying elements on M s is analyzed using the GA–BP model and the shapley additive explanation method, which provides strategies for studying the microstructure evolution of steel or optimizing the heat‐treatment process.

  PublisherDOI

• Rate-dependent molecular size effects govern the inverse thickness dependence of specific penetration energy in nanoscale thin films

  Extreme Mechanics Letters · 2025-11-11 · 1 citations

  article
  PublisherDOI

• Uncertainty-Aware Digital Twins: Robust Model Predictive Control Using Time-Series Deep Quantile Learning

  Journal of Mechanical Design · 2025-08-04 · 9 citations

  articleSenior author

  Abstract Digital twins, virtual replicas of physical systems that enable real-time monitoring, model updates, predictions, and decision-making, present novel avenues for proactive control strategies for autonomous systems. However, achieving real-time decision-making in digital twins considering uncertainty necessitates an efficient uncertainty quantification (UQ) approach and optimization driven by accurate predictions of system behaviors, which remains a challenge for learning-based methods. This article presents a simultaneous multistep robust model predictive control (MPC) framework that incorporates real-time decision-making with uncertainty awareness for digital twin systems. Leveraging a multistep-ahead predictor named time-series dense encoder (TiDE) as the surrogate model, this framework differs from conventional MPC models that provide only one-step-ahead predictions. In contrast, TiDE can predict future states within the prediction horizon in one shot, significantly accelerating MPC. Furthermore, quantile regression is employed with the training of TiDE to perform flexible and computationally efficient UQ on data uncertainty. Consequently, with the deep learning quantiles, the robust MPC problem is formulated into a deterministic optimization problem and provides a safety buffer that accommodates disturbances to enhance the constraint satisfaction rate. As a result, the proposed method outperforms existing robust MPC methods by providing less conservative UQ and has demonstrated efficacy in an engineering case study involving directed energy deposition (DED) additive manufacturing. This proactive, uncertainty-aware control capability positions the proposed method as a potent tool for future digital twin applications and real-time process control in engineering systems.

  PublisherDOI

• Co-design of geometry and thermal-elastic gradient alloy distribution with temperature-dependent material properties

  Structural and Multidisciplinary Optimization · 2025-07-01 · 2 citations

  articleOpen accessSenior author

  Abstract Additive manufacturing has enabled the fabrication of functionally graded materials (FGMs), such as compositionally graded alloys (CGAs), offering unprecedented flexibility in structural design. CGAs hold significant potential for thermal-elastic applications, yet existing design methods often overlook temperature-dependent material properties due to the complexity of coupled physics, design-dependent temperature fields, and local constraints. To address these challenges, we propose a topology optimization (TO) framework that concurrently designs geometry and graded material composition while accounting for temperature-dependent material behaviors and nonlinear thermal analysis. Our method employs a radial basis function (RBF)-based interpolation scheme to model material properties as functions of both temperature and material composition. Additionally, we leverage automatic differentiation and adjoint sensitivity analysis for computational efficiency and extensibility to GPU acceleration. Numerical examples demonstrate the effectiveness of our approach, underscoring (1) the critical role of temperature-dependent material properties in thermal-elastic structure optimization and (2) the benefits of continuous material grading in enhancing structural performance.

  PublisherOA PDFDOI

• An attention-based spatio-temporal neural operator for evolving physics

  Machine Learning Science and Technology · 2025-10-13

  articleOpen accessSenior author

  Abstract In scientific machine learning (SciML), a key challenge is learning unknown, evolving physical processes and making predictions across spatio-temporal scales. For example, in real-world manufacturing problems like additive manufacturing, users adjust known machine settings while unknown environmental parameters simultaneously fluctuate. To make reliable predictions, it is desired for a model to not only capture long-range spatio-temporal interactions from data but also adapt to new and unknown environments; traditional machine learning models excel at the first task but often lack physical interpretability and struggle to generalize under varying environmental conditions. To tackle these challenges, we propose the attention-based spatio-temporal neural operator (ASNO), a novel architecture that combines separable attention mechanisms for spatial and temporal interactions and adapts to unseen physical parameters. Inspired by the backward differentiation formula, ASNO learns a transformer for temporal prediction and extrapolation and an attention-based neural operator for handling varying external loads, enhancing interpretability by isolating historical state contributions and external forces, enabling the discovery of underlying physical laws and generalizability to unseen physical environments. Empirical results on SciML benchmarks demonstrate that ASNO outperforms existing models, establishing its potential for engineering applications, physics discovery, and interpretable machine learning.

  PublisherOA PDFDOI

• Data-driven topology optimization for multiscale biomimetic spinodal design

  Structural and Multidisciplinary Optimization · 2025-12-12 · 1 citations

  articleOpen accessSenior author

  Abstract Spinodoid architected materials have drawn significant attention due to their unique nature in stochasticity, aperiodicity, and bi-continuity. Compared to classic periodic truss-, beam-, and plate-based lattice architectures, spinodoids are insensitive to manufacturing defects, scalable for high-throughput production, functionally graded by tunable local properties, and material failure resistant due to low-curvature morphology. However, the design of spinodoids is often hindered by the curse of dimensionality with an extremely large design space of spinodoid types, material density, orientation, continuity, and anisotropy. From a design optimization perspective, while genetic algorithms are often beyond the reach of computing capacity, gradient-based topology optimization is challenged by the intricate mathematical derivation of gradient fields with respect to various spinodoid parameters. To address such challenges, we propose a data-driven multiscale topology optimization framework. Our framework reformulates the design variables of spinodoid materials as the parameters of neural networks, enabling automated computation of topological gradients. Additionally, it incorporates a Gaussian Process surrogate for spinodoid constitutive models, eliminating the need for repeated computational homogenization and enhancing the scalability of multiscale topology optimization. Compared to ‘black-box’ deep learning approaches, the proposed framework provides clear physical insights into material distribution. It explicitly reveals why anisotropic spinodoids with tailored orientations are favored in certain regions, while isotropic spinodoids are more suitable elsewhere. This interpretability helps to bridge the gap between data-driven design with mechanistic understanding. To this end, we test our design framework on several numerical experiments. We find our multiscale spinodoid designs with controllable anisotropy achieve better performance than single-scale isotropic counterparts, with clear physics interpretations.

  PublisherOA PDFDOI

• Accelerating materials discovery in heterogeneous composition-property design spaces via collaborative Bayesian optimization

  Materials & Design · 2025-12-17 · 1 citations

  articleOpen accessSenior authorCorresponding

  • Consensus-based multi-agent Bayesian optimization (BO) explores heterogeneous spaces. • Collaboration boosts efficiency, benchmarks show when simpler BO suffices. • Multi-agent BO accelerates discovery of HfTiTaNb alloys with target properties. Adaptive learning implementations for materials design are challenged by the complex, nonlinear relationships between composition and properties, particularly in high-performance applications such as high-temperature compositionally complex refractory alloys. Traditional Bayesian optimization (BO) methods, which typically rely on a single Gaussian Process (GP) surrogate, often struggle to model heterogenous behaviors across the design domain. To address this limitation, we introduce collaborative BO as a multi-agent framework for materials discovery. In the context of optimizing compositions for desired properties, each agent models a specific subregion of the design space, where subregions share similar property trends, and exchanges information with the other agents to expedite exploration and design optimization. Comparative evaluations demonstrate that, when compared to single-agent BO and other approaches discussed in this article multi-agent BO allows flexible information-sharing protocols and effectively reduces iterations of adaptive learning while reliably delivering designs that meet the targeted mechanical properties. These findings provide novel insights into the behavior of refractory multi-component alloys, using the Hf-Ti-Ta-Nb system as a case study, and illustrate the potential of adaptive multi-agent learning in efficiently screening extensive materials libraries. Moreover, the framework is broadly applicable to other problems characterized by diverse data sources, where advanced optimization strategies are essential for accelerated materials discovery.

  PublisherDOI

Recent grants

• Multidimensional Network Analysis for Analyzing and Predicting Complex Customer-Product Relations in Engineering Design

  NSF · $501k · 2014–2018

• Collaborative Research: Framework: Data: HDR: Nanocomposites to Metamaterials: A Knowledge Graph Framework

  NSF · $600k · 2018–2024

• Collaborative Research: Engineering Polymer Nanodielectric Systems Using a Descriptor-Based Design Methodology

  NSF · $491k · 2013–2017

• Stochastic Multiscale Computational Design Methodology

  NSF · $380k · 2009–2013

• BRITE Fellow: AI-Enabled Discovery and Design of Programmable Material Systems

  NSF · $1000k · 2023–2027

Frequent coauthors

• Daniel W. Apley

  65 shared

• Ping Zhu

  Soochow University

  35 shared

• Akshay Iyer

  Indian Institute of Technology Bombay

  28 shared

• L. Catherine Brinson

  Duke University

  26 shared

• Wing Kam Liu

  25 shared

• Linda S. Schadler

  20 shared

• Anton van Beek

  Northwestern University

  20 shared

• Xiaoping Du

  Space Engineering University

  18 shared

Awards & honors

• Member of American Academy of Arts and Sciences (AAA&S), ele…
• NSF BRITE Fellow, 2023
• Engineering Science Medal, Society of Engineering Science, 2…
• Charles Russ Richards Memorial Award, American Society of Me…
• Member of National Academy of Engineering (NAE), elected 201…