A Bayesian framework integrates uncertain input coordinates into Gaussian process regression to improve surrogate modeling across physical systems and differential ...
A new study in Boundary Value Problems proves the global existence and uniqueness of weak solutions for time-space fractional Kirchhoff-type ...
Researchers demonstrate that deep operator networks can accurately model cyclic adsorption processes across diverse and unseen initial conditions.
A new adaptive reduced basis trust region algorithm accelerates the solution of inverse parameter identification problems in elliptic partial differential ...
A comprehensive survey analyzes the dual synergy between domain decomposition methods and scientific machine learning algorithms for solving complex computational ...
A study applies and compares the differential transform, reduced differential transform, and second-kind Chebyshev collocation methods on telegraph equations, presents ...
Researchers have built quantum and quantum-inspired physics-informed neural networks that solve the Hamilton–Jacobi–Bellman equation from the Merton portfolio optimization problem ...
A new analysis in Boundary Value Problems establishes polynomial and exponential energy decay rates for a viscoelastic wave equation with ...
A new mathematical study identifies the exact conditions under which two-dimensional Burgers equations with external resistance terms undergo subcritical or ...
Researchers have extended Paired-Explicit Runge-Kutta time-stepping schemes to fourth-order accuracy, enabling multirate simulations that cut runtime by 66 percent to ...
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