Latest Research in Model Reduction and Neural Networks

Top Research Topics in Model Reduction and Neural Networks

• Machine Learning — 37 papers
• Model Reduction and Neural Networks — 13 papers
• Neural dynamics and brain function — 10 papers
• Numerical Analysis — 9 papers
• Chaotic Dynamics — 7 papers
• Statistical Mechanics — 6 papers
• Fluid Dynamics — 4 papers
• Quantum Physics — 4 papers
• Systems and Control — 4 papers
• Disordered Systems and Neural Networks — 4 papers

Highest-Cited Papers

1. Cleo: the fundamental design of a new superdroplet model for high computational performance (v0.39.0) (1 citations)
2. Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization
3. Going with the flow to solve for symmetry-driven PDE dynamics with physics-informed neural networks
4. Beyond Linear Dynamics: Neural Bilinear Dynamical Models for Time Series Forecasting
5. Multi-frequency far-field data enrichment for electromagnetic source reconstruction
6. Jointly Sparse Blind Deconvolution via Riemannian Optimization
7. Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations
8. Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations
9. Analysis of inverse stochastic resonance: Effects of neural excitability and timescale separation
10. Modelisation of chaotic systems with a latent Stochastic Differential Equation
11. A neural operator framework for data-driven discovery of stability and receptivity in physical systems
12. Experimental Kuramoto Platform
13. Physics‐Informed Neural Networks for Flexoelectric Boundary Value Problems: A Reformulated Architecture for High‐ and Mixed‐Order Electromechanically Coupled Systems
14. Data-driven Reverse Engineering of Physical Laws: Extracting Unknown Interactions via Hierarchical Basis Expansion
15. Phase-locking of hybrid oscillators
16. Thermalizing Stochastic Programs
17. Local sensitivity-preserving random data down-sampling for experimental design
18. Chaos Prediction: Machine Learning Versus Dynamical Models
19. A multilevel stochastic-gradient neural solver for boundary integral equations
20. Control variates with neural surrogates for uncertainty quantification in kinetic equations