1) Is there a way to determine the maximum improvement in <sign> or in S-to-N that is achievable through contour deformations and related
approaches?
2) How can ML be targeted towards such a maximal improvement?
3) Are there features of specific observables that make contour
deformation particularly effective or ineffective?
4) How do contour deformations for one observable/theory...
• Temperature check: for each panelist, in 2-5 words, how optimistic or pessimistic do you feel about the utility of ML in accelerating lattice field generation in your context.
• What are the most application-specific features of the LQCD gauge field generation problem i.e., in what important ways is it different from applications in other areas of physics, and how does that impact the way...
Panel discussion addressing the three main questions:
--Where do we stand with the identification of phases with ML?
Do you feel ML could become competitive with standard techniques for the quantitative studies of known phase transitions i.e. order of the transition at large Nf, chiral limit, etc.
--Which are your views on the need/concrete possibility of identifying hypothesised, not...
--what can EFTs do for ML4LATTICE?
--what ML4LATTICE could do for EFTs?
• What does it mean to have an ML-accelerated algorithm that is “exact”?
Discuss the distinction between exact algs, interpretable algs, and algs that allow error propagation.
• What are the differences between in-principle and in-practice exactness? Are they important?
• In what applications (both in LQFT and drawing parallels to other ares in physics) is it important to guarantee...
- Alexander Rothkopf: Setting the stage – ML for physics interpretation
- Sebastian Wetzel: ML for identifying order parameters & effective d.o.f. in the context of phase transitions
- Matteo Favoni: ML for identifying defects and effective d.o.f. in the context of confinement
- Fernando Romero Lopez: ML for extraction of emergent d.o.f. in the context of scattering resonance
- Miles...
• There is ongoing debate in the ML community about the value of trying to incorporate domain knowledge into architectures vs using computational brute force. What guides your intuition for the value of building symmetry-equivariant architectures? In your experience when is symmetry-equivariance valuable and when is it not?
• What makes gauge equivariance in particular challenging from a...
