Andreas Burger
@andreasburger.bsky.social
PhD in Toronto 🇨🇦
Simulating tiny things with ML and Quantum Computing 🧪
Ex: Singapore, Munich, Vienna 🇦🇹
Simulating tiny things with ML and Quantum Computing 🧪
Ex: Singapore, Munich, Vienna 🇦🇹
For higher-order derivatives, such as hyperpolarizability, the speed advantage of a HIP-style direct prediction approach would be even larger!
October 16, 2025 at 10:11 AM
For higher-order derivatives, such as hyperpolarizability, the speed advantage of a HIP-style direct prediction approach would be even larger!
Thank you!
Cool idea. In principle, other second-order derivatives could similarly be predicted.
The speedup compared to autodiff depends on the dimensionality of the object; for most, it won't be as large as for the Hessian.
Cool idea. In principle, other second-order derivatives could similarly be predicted.
The speedup compared to autodiff depends on the dimensionality of the object; for most, it won't be as large as for the Hessian.
October 16, 2025 at 10:10 AM
Thank you!
Cool idea. In principle, other second-order derivatives could similarly be predicted.
The speedup compared to autodiff depends on the dimensionality of the object; for most, it won't be as large as for the Hessian.
Cool idea. In principle, other second-order derivatives could similarly be predicted.
The speedup compared to autodiff depends on the dimensionality of the object; for most, it won't be as large as for the Hessian.
DEQuify your force field: More efficient simulations using deep equilibrium models
https://openreview.net/forum?id=XACVRYePQQ¬eId=pwGnz62106
t.co
April 22, 2025 at 9:09 AM
DEQuify your force field: More efficient simulations using deep equilibrium models