We utilize neural operators to learn the solution propagator for challenging systems of
differential equations that are representative of stiff chemical kinetics. Specifically, we apply …

In this paper, considering the infinite memory capability of fractional-order differential
equations and the advantages of broad echo state network, a broad fractional-order echo …

Constraint-based metabolic models have been used for decades to predict the phenotype of
microorganisms in different environments. However, quantitative predictions are limited …

Continuous Time Echo State Networks (CTESNs) are a promising yet under-explored
surrogate modeling technique for dynamical systems, particularly those governed by stiff …

Surrogate models, or machine learning based emulators of simulators, have been shown to
be a powerful tool for accelerating simulations. However, capturing the system response of …

Surrogate models are effective tools for accelerated design of complex systems. The result
of a design optimization procedure using surrogate models can be used to initialize an …

In the field of systems biology, differential equations are commonly used to model biological
systems, but solving them for large-scale and complex systems can be computationally …

Large-scale electrification is vital to addressing the climate crisis, but several scientific and
technological challenges remain to fully electrify both the chemical industry and …

Simulations drive mission-critical decision making in many fields, but are prone to
computational intractability, which severely limits an engineer's productivity whilst designing …