AEM Events

Mechanics of Growth and Growth of Mechanics

As an active research area in biomechanics, mechanics of growth studies the mechanical aspects of growth of biological tissues. While the classical theories of mechanics have provided powerful tools in studying the growth of biological tissues, the physiological process of growth, in turn, presents special challenges to the theory of mechanics, and brings forth the growth of mechanics itself. In this talk we discuss some issues that have emerged in this process. 

The existing mechanics theory of growth is based on the decomposition of the deformation gradient tensor into a growth tensor and an accommodation elastic tensor. This decomposition requires a fixed reference configuration from which the deformation gradient can be properly defined. It is built on a fundamental premise in continuum mechanics that there exists a one-to-one correspondence between the material particles in the reference configuration and in the current configuration. The assumption of the one-to-one correspondence is, however, inconsistent with a physical process of growth in which the newly deposited materials particles did not exist in a fixed reference configuration. In particular, the existing growth theory is incapable of modeling the surface growth in which changes in topology can occur as a material surface grows into a volume. In this talk, we present a theory that does not require the existence of a fixed reference configuration. 

To set the stage for the new growth theory, we first discuss a theory of elasticity that does not require a fixed reference configuration. In this theory, the movements of the material particles are described by the velocity field in the current configuration. The constitutive theory does not rely on the concept of a natural state and a fixed reference configuration. Instead, it uses the current configuration as reference. As the elastic body deforms, the response function, which gives the stress tensor in terms of the deformation gradient, is constantly updated. We derive an evolution equation for the response function. 

The proposed growth theory is then developed by incorporating a growth rate field into the above elasticity theory. The growth rate field, defined on the current configuration, describes how new material particles are added to a growing elastic body in its current state. For volumetric growth, the growth rate field has its support on the growing region. In contrast, for surface growth, the growth rate field has support on a surface and is conveniently described by singular distribution functions. The velocity field may suffer jump discontinuities across the surface. The evolution equation for the response function of a growing elastic body is derived that describes how the stress changes with growth and deformation. An example of internal surface growth is presented.

Yi-chao Chen is a Professor at University of Houston. He received his Ph.D. from the University of Minnesota, and M.S. from Johns Hopkins University. His research interests include continuum mechanics, biomechanics, stability analysis, bifurcation theory, and multifunctional materials.

Bridging Scales in Mechanics: Integrating Data-Rich Experiments with Symmetry-Aware Scientific Machine Learning 

The hierarchical and heterogeneous nature of materials drives their complex deformation and failure mechanisms across multiple length and time scales. Understanding how the microstructural features of polycrystalline metals (e.g.  grain orientations, texture evolution, defect interactions) govern their macroscopic mechanical response remains a key challenge in mechanics.

Recent advances in experimental techniques, such as those in scanning electron microscopy (SEM) and in-situ characterization, now generate massive, high-resolution datasets that capture deformation processes as they unfold. However, extracting meaningful physics from these unprecedented volumes of multi-modal data requires more than traditional analysis. Conventional machine learning approaches often fail to capture the underlying physics and scale-bridging relationships that are essential for reliable predictive modeling. 

This talk explores how scientific machine learning can bridge these scales when built on trustworthy foundations. We will discuss why enforcing known physical constraints, particularly material symmetries ranging from crystallographic symmetries at the grain level to texture-induced anisotropy at larger scales, is essential for creating trustworthy models for scientific discovery. By embedding these intrinsic symmetries, models become more interpretable, data-efficient, and physically consistent, directly addressing critical limitations of black-box approaches. 

Through examples of scanning electron microscopy-based datasets, this talk will demonstrate how integrating rich experimental data with physics-informed architectures can enable trustworthy, interpretable models that respect the fundamental principles governing material behavior and reveal new insights into multi-scale deformation mechanisms.  

Honeywell Aerospace is hosting a recruiting event at the McNamara Alumni Center on Monday, October 12. This will be a unique opportunity for students to connect with industry visionaries and Honeywell Aerospace leaders, showcase academic work, and discover how Honeywell Aerospace is pioneering the future of aviation and technology. Find more information and RSVP here.

Dielectric Elastomer Actuators for Soft Robots in Extreme Aerospace Environments

Mihai "Mishu" Duduta is an assistant professor in the School of Mechanical, Aerospace and Manufacturing Engineering at UConn. He completed a BS in Materials Science and Engineering at MIT, then became the first employee of 24M Technologies, a start-up spun out to commercialize a battery technology he co-invented. His PhD thesis in soft robotics, completed at Harvard University under the guidance of Profs. Robert Wood and David Clarke, included work which won a Gold Award at the Materials Research Society Fall Meeting 2018 and was nominated for Best Paper at ICRA 2018. Before UConn he was a Medical Devices Innovation Fellow at the University of Minnesota, then an assistant professor at the University of Toronto in Mechanical & Industrial Engineering. His research group focuses on materials and manufacturing innovations that enable soft machines to operate in extreme environments. His group recently flew a soft robotic system to the Stratosphere on a high-altitude balloon and is preparing a suborbital demonstration through NASA's RockSat program. Prof. Duduta authored the book Soft Robotics: Building Machines from Soft Matter, published by De Gruyter in 2025.