
Explore stochastic modeling of an explorer's movement in inhomogeneous environments, analyzing delay equations and their implications for swarm behavior and aggregation transitions.

Explore fluctuation limits in mean-field interacting nonlinear Hawkes processes, delving into advanced mathematical concepts and their applications.

Explore connections between Gromov-Witten theory and statistical physics, applying concepts from both fields to gain new insights and tackle complex problems in topological string theory.

Explore dynamic correlators in Kitaev materials using mean-field theory. Gain insights into quantum magnetism and topological states, with applications to real materials and emergent phenomena.

Explore a novel quantum embedding approach for coupling system fragments with long-range interactions, addressing limitations in current methodologies and offering systematic improvability for more reliable results.

Explore large particle systems with Coulomb repulsion, examining mean-field dynamics and statistical mechanics using modulated energy approaches and free energy expansions.

Explore large particle systems with Coulomb repulsion, covering mean-field dynamics and statistical mechanics using modulated energy and free energy expansion approaches.

Explore Coulomb-type repulsion in large particle systems, covering mean-field dynamics and statistical mechanics with focus on free energy expansion and fluctuations.

Explore large particle systems with Coulomb repulsion, examining mean-field dynamics and statistical mechanics using modulated energy approaches and free energy expansions.

Explore recent findings on mean field theory applied to spiking neuron networks, presented by Romain Veltz from Inria Sophia Antipolis.

Explore spontaneous oscillations in excitatory neural networks through mean field analysis, uncovering insights into complex neuronal dynamics.

Explore equilibrium dynamics and gradient descent in disordered systems, focusing on theoretical concepts and practical applications in statistical physics and optimization.

Explore mean-field limits for singular flows, focusing on Coulomb and Riesz interactions in gradient and conservative systems, with insights on convergence methods.

Explore negative drag and symmetry-breaking motility in dilute active fluids, examining continuous and discontinuous transitions in object velocity and critical phenomena in the mean-field Ising universality class.

Explore the dynamics of nonequilibrium superconductivity, comparing quantum and classical descriptions through exact solutions and evaluating local observables in BCS superconductors.

Explore high-dimensional synchronization through mean-field theory of vector spin models on networks, focusing on arbitrary degree distributions.

Explore mean-field disordered systems and their connection to Hamilton-Jacobi equations in this advanced physics workshop by J.C Mourrat.

Explore effective theories in classical and quantum particle systems, focusing on correlation energy of electron gas in the mean field regime.

Explore Kikuchi's cluster variation method for Ising models and alloys, learning entropy approximations and applications to binary and high entropy alloys.

Explore Fokker-Planck-Inverse Reinforcement Learning for revealing agent behavior rationale. Learn about a novel physics-aware algorithm inferring transition and reward functions from observed trajectories.

Explore mean field limits of heterogeneous networks, focusing on interacting particle systems coupled on digraphs and their macroscopic behavior. Gain insights into Vlasov equations and digraph measures.