2 citations · 2 across the 7 of their papers we have counts for
9 papers
Driven Quantum Stars as Controlled Primitives for Real-Time Spin Dynamics
Michael Chertkov
Quantum advantage in real-time spin dynamics should be assessed against the strongest relevant classical substitutes, not merely against the qubit nature of the microscopic system.…
Analytic Bridge Diffusions for Controlled Path Generation
Michael Chertkov
Most modern bridge-diffusion methods achieve finite-time transport by specifying an interpolation, Schrodinger-bridge, or stochastic-control objective and then learning the associa…
Mean-Field Path-Integral Diffusion: From Samples to Interacting Agents
Michael Chertkov
Independent sample generation is the prevailing paradigm in modern diffusion-based generative models of AI. We ask a different question: can samples \emph{coordinate} through share…
Temporal Memory for Resource-Constrained Agents: Continual Learning via Stochastic Compress-Add-Smooth
Michael Chertkov
An agent that operates sequentially must incorporate new experience without forgetting old experience, under a fixed memory budget. We propose a framework in which memory is not a…
Generative Stochastic Optimal Transport: Guided Harmonic Path-Integral Diffusion
Michael Chertkov
We introduce Guided Harmonic Path-Integral Diffusion (GH-PID), a linearly-solvable framework for guided Stochastic Optimal Transport (SOT) with a hard terminal distribution and sof…
Adaptive Path Integral Diffusion: AdaPID
Michael Chertkov, Hamidreza Behjoo
Diffusion-based samplers -- Score Based Diffusions, Bridge Diffusions and Path Integral Diffusions -- match a target at terminal time, but the real leverage comes from choosing the…
Sampling Decisions: Exact Path-Space Control for Physics-Informed Generative Sampling
Michael Chertkov, Hamidreza Behjoo, Sungsoo Ahn
Scientific generative models must turn tractable local decisions into globally correlated samples that respect physical constraints. We introduce Sampling Decisions, a finite-horiz…
Harmonic Path Integral Diffusion
Hamidreza Behjoo, Michael Chertkov
In this manuscript, we present a novel approach for sampling from a continuous multivariate probability distribution, which may either be explicitly known (up to a normalization fa…
Mixing Artificial and Natural Intelligence: From Statistical Mechanics to AI and Back to Turbulence
Michael Chertkov
The paper reflects on the future role of AI in scientific research, with a special focus on turbulence studies, and examines the evolution of AI, particularly through Diffusion Mod…