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Energy Manifold Natural Gradient Descent: From Riemannian Optimization to Modern Neuroscience, NeuroAI and Quantum Physics

Dr. Rishabh Pathak
When Geometry, Energy, Artificial Intelligence and Neuroscience Converge Modern Artificial Intelligence is rapidly moving beyond the idea that learning simply means minimizing an error function. Increasingly, researchers are asking a deeper question: what is the structure of the space in which learning takes place? This question becomes particularly important when the system being modelled is constrained, nonlinear, dynamic, or governed by physical principles. A recent work titled “Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers” , by Zhangyong Liang and Huanhuan Gao, introduces Energy Manifold Natural Gradient Descent (EMNGD) , a mathematical framework that extends energy-based natural-gradient optimization from unconstrained Euclidean parameter spaces to constrained Riemannian parameter manifolds . At its core, the framework proposes a simple but powerful principle: An optimization algorithm should not only determine how to reduce error; it sh...
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The Mathematical Models used in 3D-0D closed-loop model for the simulation of cardiac biventricular electromechanics.

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Dr. Rishabh Pathak


 

The mathematical models to the reconstruction of cardiac muscle fiber architecture in biventricular geometries and the development of a 3D cardiac electromechanical (EM) model coupled with a 0D closed-loop model for the cardiovascular system. Here is an overview of the mathematical models discussed in the document:

 1. Fiber Generation Methods: The document outlines the methods used to reconstruct the cardiac muscle fiber architecture in biventricular geometries. Specifically, Laplace-Dirichlet-Rule-Based-Methods (LDRBMs) are employed to generate realistic fiber orientations within the heart. These methods involve solving Laplace boundary-value problems to determine the orientation of myocardial fibers based on boundary conditions on the heart's surfaces.

 2. 3D Cardiac EM Model: The document presents a detailed 3D cardiac electromechanical model that captures the biophysical processes involved in heart function. This model integrates aspects of electrophysiology, active contraction of cardiomyocytes, tissue mechanics, and blood circulation within the heart chambers. By considering these components, the model can simulate the electromechanical behavior of the heart in a comprehensive manner.

 3. 0D Closed-Loop Model: In addition to the 3D cardiac EM model, the document discusses the incorporation of a 0D closed-loop model for the cardiovascular system. This model represents the hemodynamics of the entire circulatory system using lumped parameters to simulate blood flow dynamics, pressure-volume relationships, and systemic interactions. The coupling of the 3D EM model with the 0D closed-loop model enables a holistic simulation of the heart's electromechanical activity in the context of circulatory dynamics.

 4. Numerical Approximation: The document also covers the numerical discretization strategies employed to solve the coupled 3D-0D model. This includes space and time discretizations using the Finite Element Method (FEM) with different mesh resolutions to handle the varying scales of electromechanical and hemodynamic processes. The Segregated-Intergrid-Staggered (SIS) approach is utilized to sequentially solve the core models contributing to cardiac EM and blood circulation.

 Overall, the mathematical models presented in the document provide a framework for simulating biventricular electromechanics and studying the complex interactions between the heart and the circulatory system. These models enable researchers to investigate cardiac function, electromechanical behavior, and hemodynamic responses in a comprehensive and integrated manner.


Piersanti, R., Regazzoni, F., Salvador, M., Corno, A. F., Dede', L., Vergara, C., & Quarteroni, A. (2021). 3D-0D closed-loop model for the simulation of cardiac biventricular electromechanics. *arXiv preprint arXiv:2108.01907*.

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