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Found 5 papers, 2 papers with code
Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics
In this setting, control policies aim to optimize therapeutic outcomes by tailoring the parameters of a DBS system, typically via electrical stimulation, in real time based on the patient's ongoing neuronal activity.
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference
PCP-Map models conditional transport maps as the gradient of a partially input convex neural network (PICNN) and uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives.
Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows
Chemically reacting flows are common in engineering, such as hypersonic flow, combustion, explosions, manufacturing processes and environmental assessments.
Novel Deep neural networks for solving Bayesian statistical inverse
We consider the simulation of Bayesian statistical inverse problems governed by large-scale linear and nonlinear partial differential equations (PDEs).
Fractional Deep Neural Network via Constrained Optimization
This paper introduces a novel algorithmic framework for a deep neural network (DNN), which in a mathematically rigorous manner, allows us to incorporate history (or memory) into the network -- it ensures all layers are connected to one another.
