Learning Interaction Kernels in Mean-Field Equations of First-Order Systems of Interacting Particles
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Publication:5028412
DOI10.1137/20M1377072MaRDI QIDQ5028412
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Publication date: 9 February 2022
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.15694
inverse problemnonparametric regressionreproducing kernel Hilbert spacemean-field equationinteracting particles/agents
Learning and adaptive systems in artificial intelligence (68T05) Inverse problems for PDEs (35R30) Inference from stochastic processes (62M99) Inverse problems for systems of particles (70F17)
Related Items (5)
Learning mean-field equations from particle data using WSINDy ⋮ On the coercivity condition in the learning of interacting particle systems ⋮ Identifiability of interaction kernels in mean-field equations of interacting particles ⋮ The LAN property for McKean-Vlasov models in a mean-field regime ⋮ Learning stochastic dynamics with statistics-informed neural network
Uses Software
Cites Work
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- Initiation of slime mold aggregation viewed as an instability
- Convergence to equilibrium for granular media equations and their Euler schemes
- Statistical inference for ergodic diffusion processes.
- Stochastic particle approximation of the Keller-Segel equation and two-dimensional generalization of Bessel processes
- Foundations of spline theory: B-splines, spline approximation, and hierarchical refinement
- Structure preserving schemes for nonlinear Fokker-Planck equations and applications
- Best choices for regularization parameters in learning theory: on the bias-variance problem.
- On the identifiability of interaction functions in systems of interacting particles
- Data-driven discovery of emergent behaviors in collective dynamics
- Mean-field description and propagation of chaos in networks of Hodgkin-Huxley and FitzHugh-Nagumo neurons
- Probabilistic approach for granular media equations in the non-uniformly convex case
- Macroscopic and large scale phenomena: coarse graining, mean field limits and ergodicity. Based on the presentations at the summer school, Enschede, the Netherlands, 2012
- Mean Field Simulation for Monte Carlo Integration
- Heterophilious Dynamics Enhances Consensus
- Inferring interaction rules from observations of evolutive systems I: The variational approach
- Learning Theory
- Inverse problem robustness for multi-species mean-field spin models
- Aggregation-Diffusion Equations: Dynamics, Asymptotics, and Singular Limits
- Nonparametric inference of interaction laws in systems of agents from trajectory data
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