Analysis of an optimal control problem for the tridomain model in cardiac electrophysiology

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Unmyelinated fibers account for a remarkable fraction of the peripheral nervous system and their activity is linked to many autonomic and somatic functions. While electrical recording of such activity from human-sized peripheral nerves holds significant po ...

We present a combination technique based on mixed differences of both spatial approximations and quadrature formulae for the stochastic variables to solve efficiently a class of optimal control problems (OCPs) constrained by random partial differential equ ...

Background: Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors ...

Neocortical neurons can increasingly be divided into well-defined classes, but their activity patterns during quantified behavior remain to be fully determined. Here, we obtained membrane potential recordings from various classes of excitatory and inhibito ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

Background Superimposition of farfield (FF) and nearfield (NF) bipolar voltage electrograms (BVE) complicates the confirmation of pulmonary vein (PV) isolation after catheter ablation of atrial fibrillation. Our aim was to develop an automatic algorithm ba ...

We are constantly bombarded by sensory information and constantly making decisions on how to act. In order to optimally adapt behavior, we must judge which sequences of sensory inputs and actions lead to successful outcomes in specific circumstances. Neuro ...

Programming intelligent robots requires robust controllers that can achieve desired tasks while adapting to the changes in the task and the environment. In this thesis, we address the challenges in designing such adaptive and anticipatory feedback controll ...

Non-noble metal catalysts (NNMCs) hold the potential to replace the expensive Pt-based materials currently used to speed up the oxygen reduction reaction (ORR) in proton exchange membrane fuel cell (PEMFC) cathodes, but they feature poor durability that in ...

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

In the context of SARS-CoV-2 pandemic, mathematical modelling has played a funda-mental role for making forecasts, simulating scenarios and evaluating the impact of pre-ventive political, social and pharmaceutical measures. Optimal control theory represent ...

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

Hydrated lipid bilayer membranes are crucial components of cells and organelles, serving as the outer boundary that separates the cellular components from the extracellular environment. Lipid membranes regulate their structures and functions by dynamically ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

We propose a mathematical and numerical model for the simulation of the heart function that couples cardiac electrophysiology, active and passive mechanics and hemodynamics, and includes reduced models for cardiac valves and the circulatory system. Our mod ...

In this work, we present a PDE-aware deep learning model for the numerical solution to the inverse problem of electrocardiography. The model both leverages data availability and exploits the knowledge of a physically based mathematical model, expressed by ...

This paper proposes a Control by Interconnection design, for a class of constrained Port-Hamiltonian systems, which is based on an associated Model Predictive Control optimization problem. This associated optimization problem allows to consider both state ...

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

The medial amygdala (MeA) receives pheromone information about conspecifics and has crucial functions in social behaviors. A previous study showed that activation of GABA neurons in the postero-dorsal MeA (MeApd) with channelrhodopsin-2(H)(134R) (ChR2) sti ...

Adjustable robust minimization problems where the objective or constraints depend in a convex way on the adjustable variables are generally difficult to solve. In this paper, we reformulate the original adjustable robust nonlinear problem with a polyhedral ...

Analysis of an optimal control problem for the tridomain model in cardiac electrophysiology

Chat with Graph Search

Biophysical characterization of the recording of unmyelinated and myelinated fiber activity with peripheral interfaces

A Combination Technique for Optimal Control Problems Constrained by Random PDEs

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing

Distributionally Robust Linear Quadratic Control

A single-beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms from the pulmonary veins

Striatal Dopamine Signals and Reward Learning

Learning and optimization of anticipatory feedback controllers for robot manipulation

Decoupling the Contributions of Different Instability Mechanisms to the PEMFC Performance Decay of Non-noble Metal O2-Reduction Catalysts

Data-driven Methods for Control: from Linear to Lifting

Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: An application to the Italian scenario

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Effects of thermal, mechanical, and electrostatic perturbations on lipid membrane hydration investigated by second harmonic imaging

Universal and adaptive methods for robust stochastic optimization

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

A combined Control by Interconnection—Model Predictive Control design for constrained Port-Hamiltonian systems

Memory of Motion for Initializing Optimization in Robotics

Stimulation of medial amygdala GABA neurons with kinetically different channelrhodopsins yields opposite behavioral outcomes

Dual Approach for Two-Stage Robust Nonlinear Optimization