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Developments
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JIPS #20 - Alexander Dunlap
Plamen Turkedjiev: Least squares regression Monte Carlo for approximating BSDES and semilinear PDES
Backward Stochastic Differential Equations
Functional Stochastic Differential Equations
David Duvenaud - Latent Stochastic Differential Equations: An Unexplored Model Class
Hybrid sparse stochastic processes and the resolution of (...) - Unser - Workshop 2 - CEB T1 2019
Feb 11, 2022 - Cell-average based neural network fast solvers for time dependent PDEs
Lars Ruthotto: Deep Neural Networks Motivated By Differential Equations (Part 2/2)
Deep Learning and Computations of High Dimensional Partial Differential Equations (PDEs)
Detailed Analysis
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Last Updated: August 16, 2026
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