Mixed stochastic system design

Our goal is to establish the first-of-its-kind design methodology that (i) tailors the structural stochasticity and morphology simultaneously to achieve optimal performances and (ii) enables designing mixed stochasticity structural/microstructural systems. In our vision, this research will also lead to the automation of the nature/bio-inspired design process.

 

Ditigal and Cyber Manufacturing Systems: Resilience, AI-assisted Modeling, and Process Design

New! Schematic to be added.

 

 

Stochastic modeling and computational design of microstructural materials

Our goal is to develop computational methods to enable microscopic image-based statistical characterization, stochastic reconstruction, numerical modeling, and uncertainty quantification of the heterogeneous microstructural materials. In addition, we are also interested in physics-informed statistical learning methods to discover the processing-microstructure-property relation for computational material design.

Computational modeling of microstructural materials in energy storage systems
Our goal is to establish a digital twin of Li-ion battery microstructures to enable performance prediction and inverse optimization.
– Statistical characterization and stochastic reconstruction of digital microstructures to predict mechanical and electrochemical properties;
– Property prediction and inverse design based on machine learning;
– Manufacturing process simulation for electrode material design.
Deep learning-driven design of metamaterial systems considering manufacturing uncertainties
– Deep learning-based discovery and design of cellular metamaterials to achieve superior performances (e.g. elastic wave trapping, impact energy absorption);
– Manufacturing process modeling to understand its impact on material microstructures and properties;
– Uncertainty quantification of the spatially-correlation random quantities in complex topological structures.
Uncertainty quantification, uncertainty propagation, and design for reliability/robustness
The purpose is to provide computational methods (enablers) for the aforementioned research topics.
List of projects (as of 9/24/2024):
  • NSF – CAREER: Bridging the Gap between Deterministic and Stochastic Structures for Mixed Stochasticity System Design (ongoing)
  • NSF – Collaborative Research: Understanding the deformation-morphology-conductivity relationship in multistage forming-based manufacturing of conformal electronics (ongoing)
  • NSF – ENG-EAM: Collaborative Research: Embedding Stoppage-free Resilience to Stealthy Cyberphysical Attacks in Digital Manufacturing Systems (ongoing)
  • DOE – Control of Aerospace Manufacturing Variability Using Physics-Informed Artificial Intelligence (ongoing)
  • NIUVT – Modeling, Experiments and Prototype Development of Near Theoretical Capacity Lithium Ion Batteries for Unmanned Underwater Vehicles (ongoing)
  • GVSC via DREAM Center – Computational Design of Stochastically Graded Structures for Stress Wave Manipulation (ongoing)
  • GVSC via DREAM Center – Modeling and Design of fluid infilled complex structures for mechanical performances (ongoing)
  • NIUVT – Design, Additive Manufacturing, and Testing of Phononic Bandgap Metamaterials for Controlling Mechanical Waves (ongoing)
  • Ford Motor Company – Computational design of lattice structures for vehicle impact performance with structural uncertainties induced by AM process (completed)
  • DOED – UConn-GAANN: EnCoDiT: Engineering Cognitive Digital Twin Technologies For Predictive Design And Manufacturing (completed)
  • SHAP3D – Deep Generative Design of Cellular Metamaterials for AM (completed)
  • UConn CARIC internal grant – Virtual Investigation of Structures Using an Intelligent and Optimized Digital Network (completed)
  • UConn REP internal grant – Elastic Metamaterials as a Generic Haptic Interface for Virtual and Augmented Reality (completed)