Maximum Lyapunov Exponent for Temporal Networks
Explore the Maximum Lyapunov Exponent concept in temporal networks, delving into complex systems analysis and network dynamics.
Learn One Size to Infer All: Exploiting Symmetries in Dynamical Systems Using Scalable Neural Networks
Explore symmetries in dynamical systems using scalable neural networks to infer patterns across various sizes and complexities.
Optimal Control of Lake Eutrophication
Explore strategies for managing lake ecosystems, focusing on controlling eutrophication through mathematical modeling and optimal control theory techniques.
Data-Driven Closure Modeling Using Derivative-free Kalman Methods
Explore data-driven closure modeling for complex dynamical systems using derivative-free Kalman methods. Learn to improve predictions in turbulence and cloud dynamics with advanced machine learning techniques.
Progress in Graphs and Networks
Explore advancements in graph theory and network analysis, uncovering applications in various fields and enhancing problem-solving skills.
Topological Perspectives on Networks
Explore algebraic topology tools for network analysis, focusing on their application in neuroscience to understand structure-function relationships.
Topological Perspectives on Networks
Explore algebraic topology tools for network analysis, focusing on their application in neuroscience to understand structure-function relationships.
Computational Mathematics: Dynamics, Geometry, and Algebra in Numerical Integration
Explore computational mathematics at the intersection of dynamics, geometry, and algebra, focusing on geometric numerical integration and structure-preserving discretization of differential equations.
Modeling X with Partial Differential Equations
Explore advanced PDE modeling techniques using NDSolve and finite element methods for engineering applications, including axisymmetric regions and hyperelastic solid mechanics.
New Developments in BifurcationKit.jl - JuliaCon 2024
Explore advancements in BifurcationKit.jl for analyzing periodic and homoclinic orbits, including new methods for bifurcation analysis and integration with other Julia packages.
Dynamical Spectral Transition for Optimization in Very High Dimensions
Explore dynamical spectral transitions in high-dimensional optimization, examining mathematical concepts and their applications in complex systems and data analysis.
Mathematical Analysis of Models for Living Tissues and Free Boundary Problems - Tutorial Part 1
Explore mathematical models for living tissues and free boundary problems with Professor Perthame, delving into advanced analysis techniques and applications in biological systems.
Mathematical Analysis of Models for Living Tissues and Free Boundary Problems - Tutorial Part 2
Explore mathematical models for living tissues and free boundary problems with Professor Benoît Perthame. Gain insights into advanced analytical techniques for biological systems.
PDEs for Neural Assemblies - Analysis, Simulations, and Behavior
Explore PDEs for neural assemblies, analyzing their behavior through simulations and mathematical techniques with Professor Benoît Perthame from Sorbonne University.
Low-Dimensional Approximations of the Conditional Law of Volterra Processes - A Non-Positive Curvature Approach
Explore non-positive curvature approaches for approximating conditional laws of Volterra processes in low dimensions, with applications in machine learning and dynamical systems.
Combinatorial Topological Dynamics
Explore advanced concepts in combinatorial topological dynamics, focusing on machine learning applications and dynamical systems theory in this symposium presentation by Marian Mrozek.
Data-Driven Reduced Order Models of Forced Systems Using Invariant Foliations
Explore innovative techniques for creating data-driven reduced order models of forced systems, focusing on the application of invariant foliations in dynamical systems analysis.
On the Barriers of Robust Koopman Learning
Explore barriers in robust Koopman learning with Matthew Colbrook, examining challenges and advancements in machine learning for dynamical systems.
The Operator is the Model - Dynamical Systems and Machine Learning
Explore the intersection of machine learning and dynamical systems with Igor Mezic, focusing on operator-based modeling approaches and their implications for complex system analysis.
On Finite-Dimensional Approximations of Push-Forwards on Locally Analytic Functionals
Explore finite-dimensional approximations of push-forwards on locally analytic functionals in this advanced mathematical symposium presentation by Isao Ishikawa.
Statistical Learning of Transfer Operators and the Infinitesimal Generator
Explore statistical learning techniques for transfer operators and infinitesimal generators in dynamical systems, focusing on advanced mathematical concepts and applications.