@article{choudhary_radhakrishnan_lindner_sinha_ditto_2023, title={Neuronal diversity can improve machine learning for physics and beyond}, volume={13}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-023-40766-6}, abstractNote={Abstract}, number={1}, journal={SCIENTIFIC REPORTS}, author={Choudhary, Anshul and Radhakrishnan, Anil and Lindner, John F. and Sinha, Sudeshna and Ditto, William L.}, year={2023}, month={Nov} }
 @article{murali_ditto_sinha_2022, title={Reconfigurable Noise-Assisted Logic Gates Exploiting Nonlinear Transformation of Input Signals}, volume={18}, ISSN={["2331-7019"]}, url={https://doi.org/10.1103/PhysRevApplied.18.014061}, DOI={10.1103/PhysRevApplied.18.014061}, abstractNote={We demonstrate the direct implementation of all basic logical operations utilizing a single bistable system driven by nonlinearly transformed input signals, in the presence of noise. Exploiting the hopping between the dynamical states of the bistable system, assisted by the noise floor, in response to the transformed inputs, allows the implementation of the full set of logic operations. So this idea can form the basis of the design of a dynamical computing element that can be rapidly morphed to yield any desired logic gate by varying just a single control parameter. Further, the results are verified in electronic circuit experiments, demonstrating the robustness of the concept and the potential of this idea to be realized in wide-ranging systems.}, number={1}, journal={PHYSICAL REVIEW APPLIED}, author={Murali, K. and Ditto, W. L. and Sinha, Sudeshna}, year={2022}, month={Jul} }
 @article{murali_rajasekar_aravind_kohar_ditto_sinha_2021, title={Construction of logic gates exploiting resonance phenomena in nonlinear systems}, volume={379}, ISSN={["1471-2962"]}, DOI={10.1098/rsta.2020.0238}, abstractNote={A two-state system driven by two inputs has been found to consistently produce a response mirroring a logic function of the two inputs, in an optimal window of moderate noise. This phenomenon is called logical stochastic resonance (LSR). We extend the conventional LSR paradigm to implement higher-level logic architecture or typical digital electronic structures via carefully crafted coupling schemes. Further, we examine the intriguing possibility of obtaining reliable logic outputs from a noise-free bistable system, subject only to periodic forcing, and show that this system also yields a phenomenon analogous to LSR, termed Logical Vibrational Resonance (LVR), in an appropriate window of frequency and amplitude of the periodic forcing. Lastly, this approach is extended to realize morphable logic gates through the Logical Coherence Resonance (LCR) in excitable systems under the influence of noise. The results are verified with suitable circuit experiments, demonstrating the robustness of the LSR, LVR and LCR phenomena.}, number={2192}, journal={PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES}, author={Murali, K. and Rajasekar, S. and Aravind, Manaoj V. and Kohar, Vivek and Ditto, W. L. and Sinha, Sudeshna}, year={2021}, month={Mar} }
 @article{choudhary_lindner_holliday_miller_sinha_ditto_2021, title={Forecasting Hamiltonian dynamics without canonical coordinates}, volume={103}, ISSN={["1573-269X"]}, DOI={10.1007/s11071-020-06185-2}, abstractNote={Conventional neural networks are universal function approximators, but they may need impractically many training data to approximate nonlinear dynamics. Recently introduced Hamiltonian neural networks can efficiently learn and forecast dynamical systems that conserve energy, but they require special inputs called canonical coordinates, which may be hard to infer from data. Here, we prepend a conventional neural network to a Hamiltonian neural network and show that the combination accurately forecasts Hamiltonian dynamics from generalised noncanonical coordinates. Examples include a predator–prey competition model where the canonical coordinates are nonlinear functions of the predator and prey populations, an elastic pendulum characterised by nontrivial coupling of radial and angular motion, a double pendulum each of whose canonical momenta are intricate nonlinear combinations of angular positions and velocities, and real-world video of a compound pendulum clock.}, number={2}, journal={NONLINEAR DYNAMICS}, author={Choudhary, Anshul and Lindner, John F. and Holliday, Elliott G. and Miller, Scott T. and Sinha, Sudeshna and Ditto, William L.}, year={2021}, month={Jan}, pages={1553–1562} }
 @article{murali_sinha_kohar_ditto_2021, title={Harnessing tipping points for logic operations}, volume={230}, ISSN={["1951-6401"]}, DOI={10.1140/epjs/s11734-021-00014-2}, number={16-17}, journal={EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS}, author={Murali, K. and Sinha, Sudeshna and Kohar, Vivek and Ditto, William L.}, year={2021}, month={Oct}, pages={3403–3409} }
 @article{miller_lindner_choudhary_sinha_ditto_2021, title={Negotiating the separatrix with machine learning}, volume={12}, ISSN={["2185-4106"]}, url={https://doi.org/10.1587/nolta.12.134}, DOI={10.1587/nolta.12.134}, abstractNote={: Physics-informed machine learning has recently been shown to efficiently learn complex trajectories of nonlinear dynamical systems, even when order and chaos coexist. However, care must be taken when one or more variables are unbounded, such as in rotations. Here we use the framework of Hamiltonian Neural Networks (HNN) to learn the complex dynamics of nonlinear single and double pendulums, which can both librate and rotate, by mapping the unbounded phase space onto a compact cylinder. We clearly demonstrate that our approach can successfully forecast the motion of these challenging systems, capable of both bounded and unbounded motion. It is also evident that HNN can yield an energy surface that closely matches the surface generated by the true Hamiltonian function. Further we observe that the relative energy error for HNN decreases as a power law with number of training pairs, with HNN clearly outperforming conventional neural networks quantitatively.}, number={2}, journal={IEICE NONLINEAR THEORY AND ITS APPLICATIONS}, author={Miller, Scott T. and Lindner, John F. and Choudhary, Anshul and Sinha, Sudeshna and Ditto, William L.}, year={2021}, pages={134–142} }