Chemical reaction networks exhibit complex and multi-scale dynamics in critical engineering and scientific systems. Reaction-diffusion systems give rise to complex pattern-forming dynamics leveraged by natural living systems. Engineering application areas include combustion and related industrial energy systems, atmospheric chemistry of pollution and climate change models, and metabolic networks for biophysical cellular dynamics. These reaction networks are characterized by chemical species, reactions, and reaction rates, and are coupled to thermodynamic and transport processes. Researchers have focused building on accurate models and measurements for individual chemical reaction rates for decades, but when complex models are built from these components, insurmountable challenges related to high sensitivity and overparameterization have arisen. I am interested in developing more principled models for chemical reaction networks, which detail physical and chemical processes while also accounting for the complex collective behavior and sensitive and incomplete network information, leveraging machine learning and reduced-order approaches.

(a) Spiral patterns (frozen vortex chimeras) in the complex Ginzburg-Landau model for BZ chemical reaction dynamics. (b) Ignition in a methane combustion network, exhibiting sensitivity to missing reactions links. (c) Reduced network representation of the high-dimensional linear master equation dynamics for oxygen combustion network, exhibiting non-normal growth dynamics. (d) Driven chemical reaction network characterized by species concentrations and rate constants .

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