Latest Articles for Partial Differential Equations

Deep-MacroFin utilizes deep learning to solve complex economic equations effectively.

A fresh approach to tackle abrupt changes in partial differential equations.

A hybrid filter enhances numerical methods for better accuracy in discontinuous regions.

Researchers develop a method to find governing equations from noisy data.

A new method enhances neural networks for solving complex physics equations.

PINNs merge physics with data to solve complex scientific problems efficiently.

A method for risk-neutral optimization in uncertain environments.

A new approach to quantify risks in systems with uncertain parameters using Gaussian mixtures.

Neural networks enhance solutions for complex partial differential equations.

A new approach enhances predictions of dynamic systems by incorporating memory.

A novel approach using Galerkin-Boltzmann and neural networks to analyze fluid behavior.

A new method enhances neural network performance for solving complex physics equations.

A look at how financial models price options in varying market conditions.

A look at using Bayesian methods to improve experimental efficiency in various fields.

Combining traditional methods with AI to enhance PDE solutions.

A new framework combines machine learning and PDEs for efficient scientific modeling.

Optical computing offers efficient methods for solving complex partial differential equations.

This article discusses how combining methods improves operator learning effectiveness.

Rational neural networks enhance fluid dynamics accuracy and efficiency in simulations.

A method for efficient modeling of complex systems while preserving key properties.

Exploring the Unique Continuation Principle's impact on wave behavior in conductive materials.

Examining the EMI model for cell signal transmission in tissues.

An introduction to differential equations and their significance in various fields.

A new method to improve training in physics-informed neural networks.

A new approach enhances the effectiveness of deep learning in solving PDEs.

Learn how neural networks can tackle partial differential equations effectively.

A fresh approach to tackling complex control challenges efficiently.

Innovative techniques enhance control over challenging systems described by Partial Differential Equations.

A new method blends numerical techniques with neural networks for solving complex equations.

VINO combines physics and machine learning for efficient PDE solutions.

Learn how symmetry helps in simplifying complex PDEs effectively.

AMG offers a fresh approach to solving complex Partial Differential Equations efficiently.

Learn how Carleman linearization transforms complex equations into simpler forms.

Quantum computing promises new ways to address complex engineering challenges.

A new method for better robot trajectory planning using Heat Flow techniques.

Using machine learning models to predict fluid movements efficiently.

Learn how shape-morphing solutions aid in solving complex equations with real data.

Combining meta-learning and GAMs to enhance PINN solutions for complex equations.

Boundary Ehrenpreis-Palamodov Gaussian Processes improves accuracy in solving PDEs.

Learn how Trefftz-like methods simplify complex mathematical problems.