Latest Articles for Differential Equations

Research into solvable systems reveals new insights into computation and mathematical modeling.

UniFIDES simplifies solving complex fractional integro-differential equations.

Introducing a method to improve predictions of randomly collected spatiotemporal data.

A look into techniques for counting regular graphs and their applications.

This method enhances understanding of model behavior through input-output relationships.

Mathematicians seek the largest shape that fits through an L-shaped corridor.

An overview of splitting methods for solving differential equations in control systems.

Scientists are using light to move tiny particles in innovative ways.

This article discusses using machine learning to address physics problems involving differential equations.

An examination of new meromorphic forms and their properties on specific curves.

A look into two-loop five-point Feynman integrals and their role in particle physics.

A look into Feynman amplitudes and their significance in particle interactions.

This article discusses challenges in calculating three-loop form factors in particle physics.

Researchers present a faster way to solve complex differential equations using random walks.

An overview of how deep learning methods tackle differential equations.

New methods improve computations for differential equations involving Lie groups in quantum dynamics.

Innovative method combines machine learning and physics for solving differential equations.

Explore the links between shapes, transformations, and algebraic structures in mathematics.

A look into the dynamics of differential equations and equilibrium points.

Learn how homogenisation aids in understanding complex materials and systems effectively.

A new approach to understanding behavior in complicated systems using deformed statistics.

A deep dive into how wavefunctions shape our understanding of the universe.

RandNet-Parareal speeds up solving time-dependent equations efficiently.

Discover how adversarial autoencoders enhance machine learning models with limited data.

Discover how stability shapes mathematical systems and real-world phenomena.

jinns enhances physics-informed neural networks for diverse real-world applications.