Our great sponsors
-
DiffEqSensitivity.jl
Discontinued A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
One reason is that it's not robust and has some odd counter example cases that can come up where the ODE solver is able to converge rapidly on the original problem but not so rapidly in the integral sense on the derivative values. One such case showed up in this issue, which was the impetus for the change in the forward-mode sense, while the reverse sense was changed in testing with direct quadratures (which will be mentioned in a bit).
Related posts
- Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation - Chris Rackauckas (ASA Statistical Computing & Graphics Sections)
- Odd Behavior: Neural network hybrid differential equation example
- Composability in Julia: Implementing Deep Equilibrium Models via Neural Odes
- Old programming language is suddenly getting more popular again
- 2023 was the year that GPUs stood still