Numerical analysis & scientific computing
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- Mathematical modeling, simulation and optimization via (port-Hamiltonian) differential-algebraic equations
- Mixed-Noise Plug-and-Play with Infimal Convolution Fidelities and Multiple Priors
- Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection
- Some new numerical schemes for delay sub-diffusion equations via the idea of symmetry
- Optimal Transport Dropout for Structured Predictive Uncertainty
- Hierarchical low-rank compression exploiting symmetric boundary integral equations in transmission scattering
- The stability barrier of a class of Hermite-type discretizations of advection equations
- A Model-Informed Deep Learning Algorithm for Solving the Phaseless Inverse Scattering Problem
- A Spectral Model-Informed Neural Network for Inverse Source Problems
- A Structure-Preserving Symmetric Galerkin Boundary Element Method for Anisotropic Multi-phase Mullins-Sekerka Flow
- A Structure-Preserving Method of Fundamental Solutions for the Multi-Phase Mullins-Sekerka Flow
- Membrane Deformation – Finite Elements – Parametric Shape Optimization on the Koch Snowflake
- Moment-based approximation of cell and face integrals in THINC/QQ reconstruction on unstructured grids
- Finite Element Analysis of Maxwell's Equations with Time-Varying Coefficients and Nonlinear E-H Coupling: Convergence and Exponential Decay
- Neural Network-Assisted Refinement of Traditional Schemes for One-Dimensional Scalar Conservation Laws
- Tolerance Proportionality and Computational Stability in Adaptive Parallel-in-Time Runge-Kutta Methods
- Monte Carlo pricing under fast mean-reverting stochastic volatility: the multi-scale limit ε→ 0
- Deep learning methods for stochastic Galerkin approximations of random domain problems
- Fast Differentiable SVD on GPU via Polar Decomposition
- A Structure-Preserving LOBPCG Method for Computing Several of the Largest Generalized Singular Triplets
- Active Subspace-Guided Free-Form Deformation with Sinkhorn Autoencoders for Reduced-Order Modelling of Parametrised Shape Problems
- Application of Machine Learning to the Computation of Parameters in the SUPG Method for Unstructured and Anisotropic Meshes
- Why Directly Learning Periodic Trajectories Can Fail
- Finite Expression Approximation of High-Dimensional PDEs Without the Curse of Dimensionality
- Wave Breaking and Structure-Preserving Fully Discrete Method for Stochastic Camassa--Holm Equation with Additive Noise