S. I. Manukhov — accepted AI-conference papers

2026

Interaction Field Matching: Overcoming Limitations of Electrostatic Models

S. I. Manukhov, Alexander Kolesov, V.V. Palyulin, Alexander Korotin

ICLR 2026poster

Electrostatic field matching (EFM) has recently appeared as a novel physics-inspired paradigm for data generation and transfer using the idea of an electric capacitor. However, it requires modeling electrostatic fields using neural networks, which is non-trivial because of the necessity to take into…

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2025

Field Matching: an Electrostatic Paradigm to Generate and Transfer Data

Alexander Kolesov, S. I. Manukhov, Vladimir Vladimirovich Palyulin, Alexander Korotin

ICML 2025poster

We propose Electrostatic Field Matching (EFM), a novel method that is suitable for both generative modelling and distribution transfer tasks. Our approach is inspired by the physics of an electrical capacitor. We place source and target distributions on the capacitor plates and assign them positive…

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