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
The following (less minimal) example defines a system that errors with
ERROR: LoadError: MethodError: Cannot `convert` an object of type SymbolicUtils.Add{Float64, Float64, Dict{Any, Number}, Nothing} to an object of type Float64
when trying to solve it, but it can be solved when adding a PresetTimeCallback. When using remake on this system (with identical parameters), the error comes back. Full stacktrace available here.
(Issue based on this Slack discussion).
The following (less minimal) example defines a system that errors with
when trying to
solve
it, but it can be solved when adding aPresetTimeCallback
. When usingremake
on this system (with identical parameters), the error comes back. Full stacktrace available here.