RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
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In a 1D test case, backward diffusion paths converge to the infinite-pattern limit on a 1/sqrt(p) scale with infinite mean square deviation, enabling an extrapolation algorithm for density estimation and generalization.
No single post-Moore technology replaces current HPC for plasma simulations, but FPGA-class accelerators offer near-term kernel offload, non-von Neumann architectures medium-term operator acceleration, and quantum computing long-term potential for warm dense matter microphysics.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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Discovering a well-conditioned analytic continuation problem via dictionary learning
RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
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Path convergence in diffusion models
In a 1D test case, backward diffusion paths converge to the infinite-pattern limit on a 1/sqrt(p) scale with infinite mean square deviation, enabling an extrapolation algorithm for density estimation and generalization.
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Post-Moore Technologies for Plasma Simulation: A Community Roadmap
No single post-Moore technology replaces current HPC for plasma simulations, but FPGA-class accelerators offer near-term kernel offload, non-von Neumann architectures medium-term operator acceleration, and quantum computing long-term potential for warm dense matter microphysics.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.