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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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5 results for “parallel-in-time”

Parallel-in-Time Probabilistic Numerical ODE Solversmath.NA

Parallel-in-time solver reduces ODE simulation time from linear to logarithmic.

problem Efficiently solving ordinary differential equations (ODEs) with reduced computational cost.

method Formulated a parallel-in-time probabilistic numerical ODE solver using time-parallel formulation of iterated extended Kalman smoothers.

result Reduces span cost from linear to logarithmic in the number of time steps.

ODEs parallel-in-time probabilistic numerical solvers Bayesian filtering Kalman smoothing

2023-10-02abs ↗pdf ↗

Prob-GParareal: A Probabilistic Numerical Parallel-in-Time Solver for Differential Equationsstat.CO

Prob-GParareal adds uncertainty quantification to PinT solvers for differential equations.

problem Uncertainty in numerical solutions of differential equations.

method Prob-GParareal uses Gaussian processes to model Parareal correction function, providing probabilistic forecasts.

result Prob-GParareal yields accurate and robust probabilistic forecasts on various ODE systems.

numerical solvers differential equations uncertainty quantification Parallel-in-Time Gaussian processes

2025-09-04abs ↗pdf ↗

De-Sequentialized Monte Carlo: a parallel-in-time particle smootherstat.CO

dSMC improves parallel processing of state-space models.

problem Processing multiple observations efficiently in state-space models.

method A parallel-in-time particle smoother that reduces complexity to log(T).

result dSMC achieves O(log(T)) time complexity on parallel architectures.

parallel-in-time state-space models particle smoothers Monte Carlo parameter inference

2022-02-04abs ↗pdf ↗

RandNet-Parareal: a time-parallel PDE solver using Random Neural Networksstat.CO

RandNet-Parareal uses neural networks to speed up time-parallel PDE solving.

problem Solving systems of time-dependent differential equations efficiently.

method Combines Parareal's sequential and parallel approach with random neural networks.

result Achieves up to 125x and 22x speedup compared to existing methods.

time-parallel PDE Parareal random neural networks speedup

2024-11-09abs ↗pdf ↗

Universal Transformerscs.CL

Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been show…

2018-07-10abs ↗pdf ↗