Sudeshna Sinha

Choudhary, A., Radhakrishnan, A., Lindner, J. F., Sinha, S., & Ditto, W. L. (2023). Neuronal diversity can improve machine learning for physics and beyond. SCIENTIFIC REPORTS, 13(1). https://doi.org/10.1038/s41598-023-40766-6

Murali, K., Ditto, W. L., & Sinha, S. (2022). Reconfigurable Noise-Assisted Logic Gates Exploiting Nonlinear Transformation of Input Signals. PHYSICAL REVIEW APPLIED, 18(1). https://doi.org/10.1103/PhysRevApplied.18.014061

Murali, K., Rajasekar, S., Aravind, M. V., Kohar, V., Ditto, W. L., & Sinha, S. (2021). Construction of logic gates exploiting resonance phenomena in nonlinear systems. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 379(2192). https://doi.org/10.1098/rsta.2020.0238

Choudhary, A., Lindner, J. F., Holliday, E. G., Miller, S. T., Sinha, S., & Ditto, W. L. (2021). Forecasting Hamiltonian dynamics without canonical coordinates. NONLINEAR DYNAMICS, 103(2), 1553–1562. https://doi.org/10.1007/s11071-020-06185-2

Murali, K., Sinha, S., Kohar, V., & Ditto, W. L. (2021). Harnessing tipping points for logic operations. EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 230(16-17), 3403–3409. https://doi.org/10.1140/epjs/s11734-021-00014-2

Miller, S. T., Lindner, J. F., Choudhary, A., Sinha, S., & Ditto, W. L. (2021). Negotiating the separatrix with machine learning. IEICE NONLINEAR THEORY AND ITS APPLICATIONS, 12(2), 134–142. https://doi.org/10.1587/nolta.12.134