Chemical reaction networks and opportunities for machine learning
Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …
between them, are widely used to interrogate chemical systems. To capture increasingly …
On neural differential equations
P Kidger - arXiv preprint arXiv:2202.02435, 2022 - arxiv.org
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
Universal differential equations for scientific machine learning
In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …
[HTML][HTML] Two heads are better than one: current landscape of integrating QSP and machine learning: an ISoP QSP SIG white paper by the working group on the …
Quantitative systems pharmacology (QSP) modeling is applied to address essential
questions in drug development, such as the mechanism of action of a therapeutic agent and …
questions in drug development, such as the mechanism of action of a therapeutic agent and …
Stiff-pinn: Physics-informed neural network for stiff chemical kinetics
The recently developed physics-informed neural network (PINN) has achieved success in
many science and engineering disciplines by encoding physics laws into the loss functions …
many science and engineering disciplines by encoding physics laws into the loss functions …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
[HTML][HTML] Physics-informed attention-based neural network for hyperbolic partial differential equations: application to the Buckley–Leverett problem
R Rodriguez-Torrado, P Ruiz, L Cueto-Felgueroso… - Scientific reports, 2022 - nature.com
Physics-informed neural networks (PINNs) have enabled significant improvements in
modelling physical processes described by partial differential equations (PDEs) and are in …
modelling physical processes described by partial differential equations (PDEs) and are in …
Learning stiff chemical kinetics using extended deep neural operators
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 …
differential equations that are representative of stiff chemical kinetics. Specifically, we apply …
Effectively modeling time series with simple discrete state spaces
Time series modeling is a well-established problem, which often requires that methods (1)
expressively represent complicated dependencies,(2) forecast long horizons, and (3) …
expressively represent complicated dependencies,(2) forecast long horizons, and (3) …
Physics-informed neural networks and functional interpolation for stiff chemical kinetics
This work presents a recently developed approach based on physics-informed neural
networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical …
networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical …