Our group applies mathematical modeling, computational optimization, and uncertainty quantification paradigms to several chemical engineering application related to sustainable energy. Our work spans diverse length and timescales and is highly collaborative.
Uncertainty Quantification, Machine Learning, and Optimization under Uncertainty
Auto-regressive Guassian Processes (GP) for probabilstic time-series forecasts of multiscale energy markets.
Dynamic mode decomposition (DMD) is an equation-free data-driven technique to extract salient spatiotemporal dynamics, enabling accurate fast price forecasts with less data than other machine learning approaches.
Optimal design of experiments to inform characterization and scale-up of novel sorbents for carbon capture.
Bayesian hybrid models combine physics-based equations with machine learning constructs to correct systematic biases (epistemic uncertainty).
Enabling novel separations for hydrofluorocabon recycling through integrated molecular simulations, superstructure optimization, and thermophysical property measurements.
Leveraging equation-oriented process optimization to quantify water quality, energy requirements, and capital cost trade-offs and set molecular design targets for transformative membrane-free directional solvent extraction technologies for sustainable desalination.
Accelerating novel technology development by bridging the gap between molecular engineering and process systems engineering through superstructure optimization, inverse molecular design, and uncertainty quantification.
Leveraging mathematical modeling and dynamic optimization to design modular technologies to utilize stranded shale gas resources.
National Science Foundation CBET-1941596, CAREER: Uncertainty Quantification and Optimization with Hybrid Models for Molecular-to-Systems Engineering (PI: A. Dowling, Notre Dame) [link]
National Science Foundation ECC-1647722, Engineering Research Center for Innovative and Strategic Transformation of Alkane Resources - CISTAR (PI: F. Riberio, Purdue) [link]
National Science Foundation, DMS-2029814, MODULUS: Integrative multiscale modeling and multimodal experiments to decode systems-level molecular mechanisms of epithelial systems (PI: M. Alber, U.C. Riverside) [link]
National Science Foundation, EFMA-2203670, EAGER GERMINATION: Immersive Training Studio for Technology-Environment-Energy-Water-Society (TEEWS) Grand Challenges (PI: A. Dowling, Notre Dame) [link]
National Science Foundation, CBET-2225601, RECODE: Vascular Differentiation and Morphogenesis Controlled with Hybrid Memristors (PI: D. Hanjaya-Putra, Notre Dame) [link]
National Science Foundation, CBET-2147605, Elucidating Molecular Design Principles for Copolymer Membranes with Solute-Tailored Selectivity for the Separations of Rare Earth Elements (PI: W. Phillip, Notre Dame) [link]
Department of Energy DE-EE0009103, Optimizing Additive Manufacturing of Thermoelectric Materials using Bayesian Optimization-Enhanced Transfer Learning (PI: T. Luo, Notre Dame)
Department of Energy, Institute for the Design of Advanced Energy Systems (PI: D. Miller, NETL) [link]
Department of Energy, Design and Optimization Infrastructure for Tightly Coupled Hybrid Systems (PI: D. Miller, NETL) [link]
Department of Energy, Carbon Capture Simulation for Industrial Impact (Lead: M. Matuszewsk, NETL) [link]
NSF-Simons Center for Quantitative Biology, Morphogenetic cartography: Mapping morphogens to tissue shape through surrogate models and optimization of model-based design of experiments (PI: J. Zartman, Notre Dame)
National Science Foundation CMMI-1932206-S001, Unifying Principles for the Design and Manufacture of Chemically-Patterned Polymeric Membrane (PI: W. Phillip, Notre Dame) [link]
National Science Foundation CBET-1917474, Collaborative Research: Development and Application of a Molecular and Process Design Framework for the Separation of Hydrofluorocarbon Mixtures (PI: E. Maginn, Notre Dame) [link]
Department of Energy DE-SC0022409, SBIR with Precisions Combustion, Inc., (PI: H. Hawa, PCI) [link]