Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
Discretized PDEs generate dict-based solutions of the form
sol.u[dv(iv1,iv2)][idx1,idx2]
which do not allow use of solver optionsave_idx = [idx_n]
.This makes parameter fitting more challenging and would be a very great feature to get back.