Quantum Computing for Engineering Mechanics

Sachin S. Bharadwaj

New York University

Abstract – The past two decades have pointed to a future in which quantum computers may solve certain problems more efficiently than their classical counterparts, owing to substantial gains in memory and computational speed. A more pressing question, however, is whether these advantages can be translated into solutions for practical problems, for instance in engineering mechanics. To begin addressing this question, our work over the last few years has focused on fluid mechanics—a computationally demanding testbed that brings together many of the challenges that arise more broadly across engineering mechanics. In this talk, we begin with a brief pedagogical introduction to the subject, and then discuss the progress we have made so far. A central theme has been the development of end-to-end methods that preserve a net quantum advantage from input to output, while accounting for both the limitations of current and near-term quantum hardware and the fundamental discord between the linearity of quantum mechanics and the nonlinearity of the governing problems. We will highlight several contributions from our work, including efficient quantum algorithms for time-dependent nonlinear differential equations, variational and optimization-based methods, and algorithms to compute physical observables such as energy dissipation and to detect extreme events. These efforts span theory, simulation, and experiments on real quantum devices. We conclude with a forward-looking vision for integrating quantum computing with machine learning through hybrid quantum-classical architectures for future engineering applications.

Speaker Bio — Dr. Sachin S. Bharadwaj, is presently a postdoctoral associate at the Department of Mechanical and Aerospace Engineering at New York University. He obtained his PhD in 2024, from NYU under the supervision of Prof. Katepalli R. Sreenivasan. Prior to that, he obtained his undergraduate degree in 2019 in Mechanical Engineering and Theoretical Physics & Mathematics from India. He has served as the Chair of the Forum on Graduate Student Affairs (FGSA) at the American Physical Society (APS), during which he has served on various committees and spear-headed important organizational activities, at various levels. His research lies at the intersection of quantum computation and fluid dynamics (QCFD), with a broader focus on integrating quantum computing and machine learning to pave the way for the next-generation, high-performance computational research for engineering applications. Some recognitions include, the APS Five Sigma Physicist Honor, elected full membership of Sigma Xi (Honor Society), as well as being named the 2019 Rhodes Scholarship Finalist (India), jointly by the Rhodes Trust and University of Oxford, UK. He has also been selected as a NATO lecturer (2022 & 2026) at the von Karman Institute for Fluid Dynamics (VKI), Belgium.

ME512 Seminar Series
Distinguished Speaker

Donald Siegel

University of Texas at Austin

Li metal-solid state batteries (LMSSB) require that interfacial contact between the Li metal anode and the solid electrolyte (SE) be maintained during cycling. A reduction in contact area during Li stripping increases the local current density during subsequent Li plating, fostering dendrite nucleation. The contact area is influenced by the rate of Li transport within the anode toward the interface. Relevant transport mechanisms include diffusion and creep, with faster rates of these processes resulting in improved performance. Given the importance of these transport modes, predicting them as a function of the anode’s microstructure, stress state, and temperature is helpful in the design of LMSSB.

Here, the rates of diffusion and creep in Li are predicted using atomic-scale simulations. A primary goal is to understand if and how Li microstructure impacts the performance of LMSSB. First, molecular dynamics is used to estimate the rate of Li diffusion along dislocations and grain boundary triple junctions. By combining this data with that from a prior study of grain boundary diffusion, the dominant diffusion mechanisms and overall rates of self-diffusion in Li polycrystals are predicted as a function of grain size, grain shape, dislocation density, and temperature. A 1D continuum model for interfacial contact is parameterized using the computed diffusion data. The model predicts that high dislocation densities (~10¹²/cm²) and/or small grain sizes (~10 µm) enable achieving battery performance targets.

Secondly, the dominant creep deformation mechanisms are predicted as a function of applied stress, grain size, and temperature. Grain boundary sliding and Coble creep are observed to be the primary mechanisms for micron-sized grains. Finally, a kinetic lattice Monte Carlo model is developed to monitor the dynamics of Li voids as a function of interfacial thermodynamics and the presence of grain boundaries.

BIO
Don Siegel is Professor and Chair of the Walker Department of Mechanical Engineering at the University of Texas at Austin. He also has appointments in the Oden Institute for Computational Engineering and Sciences and the Texas Materials Institute. At UT, he is a Temple Foundation Endowed Professor and holds a Cockrell Family Chair for Departmental Leadership. Prior to joining UT in 2021, Prof. Siegel spent 12 years at the University of Michigan, with earlier posts in industry (Ford Motor Company) and at national laboratories (Sandia National Laboratories and the U.S. Naval Research Laboratory). Siegel is a computational materials scientist whose research targets the development of energy storage materials and lightweight alloys. He is a recipient of the NSF CAREER Award and a Gilbreth Lectureship from the National Academy of Engineering.

Abstract:

Robots that operate reliably in the real world must reason about forces, not just positions. I will present a robotics architecture designed around force intelligence—the ability to decide when, where, and how much force to apply. I will show how this perspective unifies whole-body control and dexterous manipulation, and argue that force-centric design is a key missing ingredient for scalable real-world autonomy.

Bio:

Pulkit Agrawal is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. He earned his Ph.D. from UC Berkeley and co-founded Eka Robotics and SafelyYou. Pulkit completed his bachelor’s from IIT Kanpur and was awarded the Director’s Gold Medal. His work has received multiple Best Paper Awards, the IEEE Early Career Award in Robotics and Automation, the IROS Toshio Fukuda Young Professional Award, the IIT Kanpur Young Alumnus Award, the Sony Faculty Research Award, the Salesforce Research Award, the Amazon Research Award, the Signatures Fellow Award, and the Fulbright Science and Technology Award.

ME512 Seminar Series Multimaterial Additive Manufacturing for Shape-Morphing Structures and 4D Printing Monday, May 4, 2026 3:00 PM L211 Tech

ABSTRACT Body 3D printing (additive manufacturing, AM), where materials are deposited in a layer-by-layer manner to form a 3D solid, has seen significant advances in recent decades. Multimaterial 3D printing has attracted significant research efforts in recent years. It offers the advantage of placement of materials with different properties in the 3D space with high resolution, or controllable heterogeneity. In this talk, we present our recent progress in developing multimaterial additive manufacturing methods. In the first approach, we present a new development where we integrate two AM methods, direct-ink-write (DIW) and digital light processing (DLP), into one system. In this system, the DLP can be used to print complex bulk parts while DIW can be used to print functional inks, such as conductive inks and liquid crystal elastomers. In the second approach, we recently developed a grayscale DLP (gDLP) 3D printing method where we use light intensity to control local properties and thus create structures with gradient material properties. We further investigate how to use machine learn to help the inverse design of 4D printing of shape-morphing structures with multimaterial additive manufacturing. BIO Dr. H. Jerry Qi is the Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology and is the site director of NSF IUCRC on Science of Heterogeneous Additive Printing of 3D Materials (SHAP3D). He received his undergraduate and graduate degrees from Tsinghua University and a ScD degree from MIT. After one-year postdoc at MIT, he joined the University of Colorado Boulder in 2004 and moved to Georgia Tech in 2014. Prof. Qi’s research is in the broad field of nonlinear mechanics of polymeric materials and focuses on developing fundamental understandings of multi-field properties of active polymers through experimentation and constitutive modeling, then applying these understandings to application designs. He has been working on a range of active polymers, including shape memory polymers, light-activated polymers, and covalent adaptable network polymers, for their interesting behaviors such as shape memory, light actuation, healing, reprocessing, and recycling. In recent years, he has been working on integrating active materials with 3D printing. He and his collaborators pioneered the 4D printing concept. He is a recipient of NSF CAREER award (2007), Sigma Xi Best Faculty Paper Award (2018), Gerhard Kanig Lecture by the Berlin-Brandenburg Association for Polymer Research (2019), the James R. Rice Medal from Society of Engineering Science (2023), the T. H. H. Pian Award from International Conference on Computational & Experimental Engineering and Sciences (2024), and the ASME Warner T. Koiter Medal (2024). He was listed as one of the highly cited researchers by Clarivate in 2024 and 2025

ABSTRACT

GScientific Machine Learning (SciML) integrates data-driven inference with physical modeling to solve complex problems in science and engineering. However, the design of SciML architectures, loss formulations, and training strategies remains an expert-driven research process, requiring extensive experimentation and problem-specific insights. We introduce AgenticSciML, a collaborative multi-agent system in which over 10 specialized AI agents collaborate to propose, critique, and refine SciML solutions through structured reasoning and iterative evolution. The framework integrates structured debate, retrieval-augmented method memory, and ensemble-guided evolutionary search, enabling the agents to generate and assess new hypotheses about architectures and optimization procedures. Across physics-informed learning and operator learning tasks, the framework discovers solution methods that outperform single-agent and human-designed baselines by up to four orders of magnitude in error reduction. The agents produce novel strategies—including adaptive mixture-of-expert architectures, decomposition-based PINNs, and physics-informed operator learning models—that do not appear explicitly in the curated knowledge base. These results show that collaborative reasoning among AI agents can yield emergent methodological innovation, suggesting a path toward scalable, transparent, and autonomous discovery in scientific computing.

BIO
George Karniadakis is from Crete. He is an elected member of the National Academy of Engineering, National Academy of Arts and Sciences, and a Vannevar Bush Faculty Fellow. He received his S.M. and Ph.D. from the Massachusetts Institute of Technology (1984/87). He was appointed Lecturer in the Department of Mechanical Engineering at MIT and subsequently joined the Center for Turbulence Research at Stanford/NASA Ames. He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continued to be a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018–), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010–), Fellow of the American Physical Society (APS, 2004–), Fellow of the American Society of Mechanical Engineers (ASME, 2003–), and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006–). He received the William Benter Award (2026), the SES G.I. Taylor Medal (2014), the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt Award (2017), the SIAM Ralf E. Kleinman Award (2015), the J. Tinsley Oden Medal (2013), and the CFD Award (2007) by the US Association for Computational Mechanics. His h-index is 160 (highest in Applied Mathematics) and he has been cited over 156,000 times.

BIO

Treasured member of the Northwestern faculty from 1977 until his death in 2014, Ted Belytschko was a central figure in the McCormick community and an internationally renowned researcher who made major contributions to the field of computational structural mechanics. One of the most cited researchers in engineering science, Belytschko developed explicit finite element methods that are widely used in crashworthiness analysis and virtual prototyping in the auto industry. He received numerous honors, including membership in the U.S. National Academy of Engineering, U.S. National Academy of Sciences, and the American Academy of Arts and Sciences. He was a founding director of the U.S. Association for Computational Mechanics, and in 2012, the association named a medal in his honor. The ASME Applied Mechanics Award was renamed the ASME Ted Belytschko Applied Mechanics Division Award in November 2007. Belytschko also served as editor-in-chief of the International Journal for Numerical Methods in Engineering, and he was co-author of the books “Nonlinear Finite Elements for Continua and Structures” and “A First Course in Finite Elements.”

ABOUT TED BELYTSCHKO
Treasured member of the Northwestern faculty from 1977 until his death in 2014, Ted Belytschko was a central figure in the McCormick community and an internationally renowned researcher who made major contributions to the field of computational structural mechanics. One of the most cited researchers in engineering science, Belytschko developed explicit finite element methods that are widely used in crashworthiness analysis and virtual prototyping in the auto industry. He received numerous honors, including membership in the U.S. National Academy of Engineering, U.S. National Academy of Sciences, and the American Academy of Arts and Sciences. He was a founding director of the U.S. Association for Computational Mechanics, and in 2012, the association named a medal in his honor. The ASME Applied Mechanics Award was renamed the ASME Ted Belytschko Applied Mechanics Division Award in November 2007. Belytschko also served as editor-in-chief of the International Journal for Numerical Methods in Engineering, and he was co-author of the books “Nonlinear Finite Elements for Continua and Structures” and “A First Course in Finite Elements.”

Co-sponsored by the Departments of Mechanical Engineering and Civil & Environmental Engineering