Latest Articles for Partial Differential Equations

A fresh approach combines machine learning with traditional fluid dynamics for better predictions.

A look into how we determine parameter values in differential equations.

Exploring techniques for solving PDEs on dynamic surfaces.

A new method improves solving complex equations using machine learning.

A new method combines neural networks and domain decomposition for solving complex PDEs.

Research on nonlocal symmetries and PDEs leads to significant insights in science.

A new method improves the efficiency of solving PDEs with deep learning.

New method improves efficiency in solving complex partial differential equations using neural networks.

A new approach to efficiently solve complex PDEs using machine learning.

This model improves predictions for partial differential equations using innovative techniques.

Using neural networks to solve complex partial differential equations efficiently.

Transolver offers advanced solutions for complex mathematical problems in various fields.

Exploring how VarQITE can solve Maxwell's equations using quantum computing.

This article discusses new methods for accurately capturing shocks in fluid dynamics.

A new method enhances quantum computing's role in solving Partial Differential Equations.

A method to manage complex systems effectively when parameters are not fully known.

New methods improve solutions for complex equations using diffusion models.

Learn how PINNs combine deep learning with physics for efficient problem-solving.

A new framework improves prediction accuracy in fluid dynamics.

This article discusses modeling thin structures like the diaphragm from medical images.

Examining snapshot methods for better modeling of complex systems over time.

Researchers aim to improve predictions of partial differential equations using Neural Operators.

Using neural networks to efficiently solve complex partial differential equations.

A method that improves approximation of mathematical models using neural networks and finite elements.

A new model improves missing data handling in time series analysis.

Differential walk on spheres offers efficient solutions for complex shape analysis.

Exploring how organisms move towards or away from chemical signals.

New methods enhance predictions and parameter estimation in physical simulations.

A new approach improves the training of neural networks for solving differential equations.

A look into how materials react under pressure using mathematical approaches.

An overview of PDEs, self-similar solutions, and their significance in various fields.

Poseidon uses machine learning to efficiently predict solutions for complex PDEs.

A method for solving complex quasiperiodic elliptic equations using the projection technique.

This study assesses deep learning models in solving complex equations efficiently.

iPOD offers a practical solution for managing data in complex simulations of PDEs.

New methods in VEM improve solutions for mathematical problems with irregular boundaries.

M2NO combines techniques to solve PDEs more accurately and efficiently.

A new technique combines numerical methods and machine learning for PDE solutions.

A new framework combines deep learning with physics to improve PDE predictions.

This article introduces a framework to tackle uncertainties in optimization with partial differential equations.