In review. Summary coming soon.
We built a differentiable Vlasov-Poisson-Fokker-Planck solver and used gradient-based optimization to discover a novel superadditive effect in nonlinear plasma wavepackets relevant to inertial fusion.
We built a differentiable Thomson-scattering analysis tool using reverse-mode automatic differentiation and GPUs, achieving >140x speedup over previous methods and enabling near real-time plasma diagnostics, Hessian-based uncertainty estimation, and direct measurement of electron velocity distribution functions.
An invited review demonstrating that differentiable programming — automatic differentiation applied to physics solvers — provides a unifying framework for discovery, closure learning, diagnostics, and inverse design in plasma physics.
We embedded a machine-learned hidden variable into a differentiable fluid solver to capture nonlinear Landau damping — a kinetic effect no fluid closure had previously reproduced. Trained on single-wavelength boxes, the model generalizes to wavepacket geometries 100x larger.