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An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner
Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
Contains code for the numerical examples in an article by Song and Khan (2024). This code evaluates subgradients for convex relaxations of parametric ordinary differential equations (ODEs)
A compressible, flat-plate, laminar boundary layer solver in Julia! This routine computes the velocity and temperature profiles with variable viscosity and thermal conductivity. The numerical results are validated against wind-tunnel measurements.