Autodifferentiable programming! Neural networks are the famous example of this, of course -- but this can be extended to all of scientific computing. ODE/SDE s

Autodifferentiable programming! Neural networks are the famous example of this, of course -- but this can be extended to all of scientific computing. ODE/SDE solvers, root-finding algorithms, LQP, molecular dynamics, ... These days I'm doing all my work in JAX. (E.g. see Equinox or Diffrax: https://github.com/patrick-kidger/equinox https://github.com/patrick-kidger/equinox, https://github.com/patrick-kidger/diffrax https://github.com/patrick-kidger/diffrax). A lot of modern work is now based around hybridising such techniques with neural networks. I'd really encourage anyone interested to learn how JAX works under-the-hood as well. (Look up "autodidax") Lots of clever/novel ideas in its design.