About

Shuwen Li received her PhD in Rhetoric and Scientific & Technical Communication from the University of Minnesota, Twin Cities. She has taught at the University of Michigan, Ann Arbor for several years. Currently, she is an Associate Professor of Instruction at the Cook Family Writing Program at Northwestern University. Her teaching includes courses such as English 282 (Writing and Speaking in Business) and DSGN 106 (Design Thinking and Communication). Her research interests encompass professional writing and communication, tactical technical communication, and technical writing pedagogy in intercultural contexts.

Research topics

• Computer Science
• Engineering
• Medicine
• Artificial Intelligence
• Structural engineering
• Political Science
• Machine Learning
• Construction engineering
• Applied mathematics
• Mathematics
• Environmental science
• Materials science
• Systems engineering
• Physics
• Composite material
• Law
• Risk analysis (engineering)

Selected publications

• Computational Study of Optimal Aggregate Ratio in the Mix Design of Various Concrete Types

  Journal of Engineering Mechanics · 2026-01-08

  articleSenior author

  The construction industry is increasingly prioritizing the enhancement of concrete materials to meet environmental goals and promote sustainability by optimizing aggregate proportion. The improvement of aggregate proportion reduces cement usage and enhances the physical, mechanical, and durability properties of concrete. This research simulated aggregate proportion to determine the optimal packing density, aiming to improve the performance of concrete. Specifically, LIGGGHTS software was used to model various aggregate proportions and identify the mixture with the highest packing density. The simulation results, which showed the variance of the experimental data, were validated against the results from the modified Andreasen and Andersen model and the experimental data. The study indicates that the optimal packing density and stress distribution between particles provides a robust framework for predicting material performance. Using these results, it is possible to identify the optimal state of aggregate composition or the optimal packing density by analyzing the gradation and specific ratios of aggregate composition, and significantly increase the accuracy of mix design and resource management. It also saves time and money. The findings from this research will help researchers facilitate the selection of optimal aggregate mixes efficiently, thereby saving both time and resources.

  PublisherDOI

• A highly efficient time-domain solution method for the evolutionary statistical moments of the long-span bridges’ buffeting responses under the non-stationary wind load

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Effect of Nanosized Fillers on Wear Resistance of Polyether Ether Ketone

  Russian Engineering Research · 2025-10-01

  article1st authorCorresponding

  The reasons for the improved wear resistance of a composite based on polyether ether ketone with a dispersed filler in the form of nanosized copper particles are studied using the molecular dynamics method. Computer simulation of the sliding friction process of polyether ether ketone and its nanocomposite is performed. It is established that in the zone of the simulated tribocontact, the density of polyether ether ketone molecules is 4 times greater than the density of its nanocomposite. Meanwhile, the energy of intermolecular interaction in the shear zone of polyether ether ketone and the metal layer exceeds this indicator during contact of the composite with a counterbody by 35%. It is shown that this dispersed filler stabilizes the kinetic state of the molecules of the polymer material and increases the wear resistance of the composite compared to the matrix.

  PublisherDOI

• Experimental investigation on structural behavior of concrete-filled S690 steel tubes

  2025-08-07

  book-chapter1st authorCorresponding

  Despite the growing interest in high-strength steel for construction, research on concrete-filled welded box sections utilizing S690 steel remains limited, particularly regarding their structural behavior and the development of reliable design guidelines. This study addresses this gap by systematically investigating the performance of S690 steel box sections, both hollow and concrete-filled (CFST), with the goal of proposing design recommendations for their use in high-rise buildings. The research program integrates experimental testing, numerical modeling, and the development of design methodologies. This paper focuses on the experimental phase, presenting results from tests conducted on four hollow steel columns and one CFST column. The findings provide critical insights into load-carrying capacities and failure mechanisms, thereby contributing to a more robust understanding of S690 welded steel box sections and informing future updates to design codes for high-performance structural applications.

  PublisherDOI

• The Peridynamic Material Correspondence Models: A State-of-the-Art Review on Stabilization Schemes

  Journal of Peridynamics and Nonlocal Modeling · 2025-03-01 · 6 citations

  reviewSenior author
  PublisherDOI

• Peridynamics modeling of ice fragmentation under blast loads

  Defence Technology · 2025-11-01

  articleOpen accessCorresponding

  This study presents a Non-Ordinary State-Based Peridynamics (NOSB-PD) and Smoothed Particle Hydrodynamics (SPH) coupling framework for simulating the dynamics of ice fragmentation under explosive blasting loads. Addressing critical limitations in conventional numerical methods for modeling fragmentation dynamics and fluid-structure interaction, the proposed PD-SPH model has some unique features: (1) A novel ice constitutive model is adapted for ice's viscoelastoplastic behavior under explosive loading; (2) Experimentally determined temperature factors were incorporated into the plastic consistency law of the yield function, and a failure criterion accounting for temperature-dependent critical strain was adopted; (3) A newly identified optimal stand-off distance is implemented, which can maximize ice-breaking efficiency, and (4) Dimensionless parameters are adopted for cross-scale analysis. By doing so, the proposed PD-SPH model demonstrates exceptional numerical accuracy in capturing shock wave propagation and ice fracture patterns. These advances provide a robust, physics-based predictive modeling tool that can simulate explosive-induced icebreaking at an engineering scale for the design and implementation of various engineering projects in cold regions.

  PublisherDOI

• A stochastic multiscale asymptotic homogenization approach to 3D printed biodegradable resin TPMS bio-inspired structures

  Thin-Walled Structures · 2025-03-10 · 15 citations

  article
  PublisherDOI

• Artificial Intelligence-Aided Design (AIAD) for Structures and Engineering: A State-of-the-Art Review and Future Perspectives

  Archives of Computational Methods in Engineering · 2025-03-18 · 17 citations

  reviewOpen access

  Abstract Even with the state-of-the-art technology of computer-aided design and topology optimization, the present structural design still faces the challenges of high dimensionality, multi-objectivity, and multi-constraints, making it knowledge/experience-demanding, labor-intensive, and difficult to achieve or simply lack of global optimality. Structural designers are still searching for new ways to cost-effectively to achieve a possible global optimality in a given structure design, in particular, we are looking for decreasing design knowledge/experience-requirements and reducing design labor and time. In recent years, Artificial Intelligence (AI) technology, characterized by the large language model (LLM) of Machine Learning (ML), for instance Deep Learning (DL), has developed rapidly, fostering the integration of AI technology in structural engineering design and giving rise to the concept and notion of Artificial Intelligence-Aided Design (AIAD). The emergence of AIAD has greatly alleviated the challenges faced by structural design, showing great promise in extrapolative and innovative design concept generation, enhancing efficiency while simplifying the workflow, reducing the design cycle time and cost, and achieving a truly global optimal design. In this article, we present a state-of-the-art overview of applying AIAD to enhance structural design, summarizing the current applications of AIAD in related fields: marine and naval architecture structures, aerospace structures, automotive structures, civil infrastructure structures, topological optimization structure designs, and composite micro-structure design. In addition to discussing of the AIAD application to structural design, the article discusses its current challenges, current development focus, and future perspectives.

  PublisherOA PDFDOI

• A Practical Finite Element Approach for Simulating Dynamic Crack Growth in Cu/Ultra Low-k Interconnect Structures

  ArXiv.org · 2025-07-31

  preprintOpen accessSenior author

  This work presents a practical finite element modeling strategy, the Crack Element Method (CEM), for simulating the dynamic crack propagation in two-dimensional structures. The method employs an element-splitting algorithm based on the Edge-based Smoothed Finite Element Method (ES-FEM) to capture the element-wise crack growth while reducing the formation of poorly shaped elements that can compromise numerical accuracy and computational performance. A fracture energy release rate formulation is also developed based on the evolving topology of the split elements. The proposed approach is validated through a series of classical benchmark problems, demonstrating its accuracy and robustness in addressing dynamic fracture scenarios. Finally, the applicability of the CEM is illustrated in a case study involving patterned Cu/Ultra Low-k interconnect structures.

  PublisherOA PDFDOI

• Towards Efficient GPU Cluster Management via Coordinated Multi-Module Elastic Scheduling

  2025-10-10

  article

  In recent years, deep neural network (DNN) training has become a widely adopted and resource-intensive workload in enterprise and cloud data centers. Efficient scheduling of GPU clusters is essential for improving resource utilization and reducing job completion time in distributed deep learning training. This paper presents elastic GPU optimization (EGO), a coordinated multi-module scheduler that optimizes GPU cluster resource scheduling and management for distributed deep learning jobs. EGO consists of three core modules: job admission control, job scheduling, and job placement. These modules collectively maximize resource utilization and minimize job completion time. Specifically, EGO includes a scheduling algorithm based on an elastic scaling policy. This algorithm dynamically allocates GPUs to admitted jobs while accounting for the diminishing returns in training throughput as the number of allocated GPUs increases. EGO also leverages model-specific preferences for job placement to mitigate performance degradation caused by network communication topology. Experimental results demonstrate that EGO reduces the average completion time of training jobs by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.07 \times$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4.56 \times$</tex> compared to existing scheduling methods.

  PublisherDOI

Recent grants

• Multiscale Simulation of Soft Contact and Cell Adhesion

  NSF · $271k · 2008–2011

• CAREER: Advance Meshfree Multiscale Methods

  NSF · $399k · 2003–2008

Frequent coauthors

• Yunlan Su

  Chinese Academy of Sciences

  50 shared

• Dujin Wang

  Institute of Chemistry

  50 shared

• Wing Kam Liu

  34 shared

• Weilong Ju

  Beijing National Laboratory for Molecular Sciences

  32 shared

• Guoming Liu

  Beijing Aerospace Flight Control Center

  25 shared

• Alejandro J. Müller

  Ikerbasque

  25 shared

• Xiangning Wen

  Beijing National Laboratory for Molecular Sciences

  18 shared

• Bo Ren

  18 shared

Education

• PhD, Department of Mechanical Engineering

  Northwestern University

  1997

• Master of Science, Department of Aerospace Engineering and Engineering Science

  University of Florida

  1994

• Master of Science, Department of Mechanics

  Huazhong University of Science and Technology

  1989

• Bachelor, Department of Mechanical Engineering

  East China University of Science and Technology

  1982