Research news on Numerical techniques

Numerical techniques are computational methods for approximating solutions to mathematical problems that lack closed-form expressions or are analytically intractable. They encompass algorithms for root finding, numerical integration and differentiation, solution of linear and nonlinear systems, eigenvalue problems, optimization, and numerical solution of ordinary and partial differential equations. These techniques rely on discretization, iteration, and error analysis, with careful attention to stability, convergence, conditioning, and computational complexity. They are implemented using floating-point arithmetic and often exploit matrix factorizations, interpolation, finite difference, finite element, or spectral methods to achieve controllable accuracy within specified tolerances.

AI makes quantum field theories computable

An old puzzle in particle physics has been solved: How can quantum field theories be best formulated on a lattice to optimally simulate them on a computer? The answer comes from AI.

New code connects microscopic insights to the macroscopic world

In inertial confinement fusion, a capsule of fuel begins at temperatures near zero and pressures close to vacuum. When lasers compress that fuel to trigger fusion, the material heats up to millions of degrees and reaches ...

Making lighter work of calculating fluid and heat flow

Scientists from Tokyo Metropolitan University have re-engineered the popular Lattice-Boltzmann Method (LBM) for simulating the flow of fluids and heat, making it lighter and more stable than the state-of-the-art.

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