Concept
Mathematical physics
Related publications (106)
Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain
Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...
EPFL2024Geometric Learning: Leveraging differential geometry for learning and control
In this thesis, we concentrate on advancing high-level behavioral control policies for robotic systems within the framework of Dynamical Systems (DS). Throughout the course of this research, a unifying thread weaving through diverse fields emerges, and tha ...
EPFL2024SC-TPTP: An Extension of the TPTP Derivation Format for Sequent-Based Calculus
Motivated by the transfer of proofs between proof systems, and in particular from first order automated theorem provers (ATPs) to interactive theorem provers (ITPs), we specify an extension of the TPTP derivation text format to describe proofs in first-ord ...
2024Disruption avoidance and investigation of the H-Mode density limit in ASDEX Upgrade
Alessandro Pau, Federico Alberto Alfredo Felici, Bernhard Sieglin
In recent years a strong effort has been made to investigate disruption avoidance schemes in order to aid the development of integrated operational scenarios for ITER. Within the EUROfusion programme the disruptive H-mode density limit (HDL) has been studi ...
Bristol2024Deep Learning Meets Sparse Regularization
Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of ...
2023Informing Neural Networks with Simplified Physics for Better Flow Prediction
Surrogate deep neural networks (DNNs) can significantly speed up the engineering design process by providing a quick prediction that emulates simulated data. Many previous works have considered improving the accuracy of such models by introducing additiona ...
2023Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks
Olga Fink, Vinay Sharma, Manav Manav
We propose a physics-informed message-passing graph neural network (GNN) for learning the dynamics of springmass systems. The proposed method embeds the underlying physics directly into the message-passing scheme of the GNN. We compare the new scheme with ...
Research Publishing2023Mechanism balancing taxonomy
Simon Nessim Henein, Florent Cosandier, Hubert Pierre-Marie Benoît Schneegans
The balancing of mechanisms consists in distributing their moving masses, inertias, and elastic components in order to achieve key mechanical properties, such as the elimination of the shaking forces and moments exported onto their supporting structure or ...
2023A mathematical theory for mass lumping and its generalization with applications to isogeometric analysis
Annalisa Buffa, Espen Sande, Yannis Dirk Voet
Explicit time integration schemes coupled with Galerkin discretizations of time-dependent partial differential equations require solving a linear system with the mass matrix at each time step. For applications in structural dynamics, the solution of the li ...
2022Mathematical Foundations of Adaptive Isogeometric Analysis
Annalisa Buffa, Rafael Vazquez Hernandez
This paper reviews the state of the art and discusses recent developments in the field of adaptive isogeometric analysis, with special focus on the mathematical theory. This includes an overview of available spline technologies for the local resolution of ...
SPRINGER2022