Author

Deepak Akhare

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Preprint Aug 2026

Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system

Control of plasma turbulence remains a long-standing challenge in magnetically confined fusion research. Here, we explore a reinforcement-learning (RL) approach for bidirectional control of the turbulence-zonal-flow transition in the modified Hasegawa-Wakatani system, a minimal model of electrostatic drift-wave turbule...

Lu-Ning Sun, Ben Zhu, Xin-Yang Liu et al. · 0 citations

Open access Aug 2026

Data-augmented few-shot neural stencil emulation for computer-model system identification

This work proposes a more sample-efficient data-augmentation strategy for generating NPDE training data from a computer model—specifically, one using an explicit, structured-grid finite-difference numerical solver—by space-filling sampling of local ‘stencil’ states.

Sanket R. Jantre, Deepak Akhare, Zhiyuan Wang et al. · Machine Learning: Science an... · 0 citations

Jun 2026

Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction

This work develops a sensor position-conditioned architecture that enables a single policy network to adapt seamlessly to multiple sensor arrangements and demonstrates that the policy extraction from the dataset provides unprecedented flexibility for sensor placement optimization.

Deepak Akhare, Luning Sun, Xin-Yang Liu et al. · arXiv.org · 0 citations