Suresh Bishnoi

Publications

  1. S. Bishnoi, S. Singh, R. Ravinder, M. Bauchy, N. N. Gosvami, H. Kodamana, N. M. A. Krishnan. "Predicting Young's modulus of oxide glasses with sparse datasets using machine learning." Journal of Non-Crystalline Solids, vol. 524, p. 119643, 2019. Elsevier. Link

  2. S. Bishnoi, R. Ravinder, H. Singh Grover, H. Kodamana, N. M. Anoop Krishnan. "Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets." Materials advances, vol. 2, no. 1, pp. 477–487, 2021. Royal Society of Chemistry. Link

  3. S. Bishnoi, S. Badge, N. M. Anoop Krishnan, and others. "Predicting oxide glass properties with low complexity neural network and physical and chemical descriptors." Journal of Non-Crystalline Solids, vol. 616, p. 122488, 2023. North-Holland. Link

  4. S. Bishnoi, R. Bhattoo, J. Jayadeva, S. Ranu, N. M. Krishnan. "Enhancing the inductive biases of graph neural ode for modeling dynamical systems." In The Eleventh International Conference on Learning Representations, 2023. Link

  5. S. Bishnoi, R. Bhattoo, J. Jayadeva, S. Ranu, N. M. Krishnan. "Learning the Dynamics of Physical Systems with Hamiltonian Graph Neural Networks." In ICLR 2023 Workshop on Physics for Machine Learning, 2023. Link

  6. S. Bishnoi, J. Jayadeva, S. Ranu, N. M. Krishnan. "BroGNet: Momentum-Conserving Graph Neural Stochastic Differential Equation for Learning Brownian Dynamics." In AI for Accelerated Materials Design - NeurIPS 2023 Workshop, 2023. Link

  7. R. Ravinder, K. H. Sridhara, S. Bishnoi, H. Singh Grover, M. Bauchy, H. Kodamana, N. M. Anoop Krishnan, and others. "Deep learning aided rational design of oxide glasses." Materials horizons, vol. 7, no. 7, pp. 1819–1827, 2020. Royal Society of Chemistry. Link

  8. R. Ravinder, S. Singh, S. Bishnoi, A. Jan, A. Sharma, H. Kodamana, N. M. Anoop Krishnan. "An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19." Heliyon, vol. 6, no. 12, 2020. Elsevier. Link

  9. M. Zaki, V. Venugopal, R. Bhattoo, S. Bishnoi, S. K. Singh, A. R. Allu, Jayadeva, N. M. Anoop Krishnan. "Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations." Journal of the american ceramic society, vol. 105, no. 6, pp. 4046–4057, 2022. Link

  10. R. Ravinder, V. Venugopal, S. Bishnoi, S. Singh, M. Zaki, H. Singh Grover, M. Bauchy, M. Agarwal, N. M. Anoop Krishnan. "Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century." International journal of applied glass science, vol. 12, no. 3, pp. 277–292, 2021. Link

  11. A. Thangamuthu, G. Kumar, S. Bishnoi, R. Bhattoo, N. M. Krishnan, S. Ranu. "Unravelling the performance of physics-informed graph neural networks for dynamical systems." Advances in Neural Information Processing Systems, vol. 35, pp. 3691–3702, 2022. Link

  12. R. Bhattoo, S. Bishnoi, M. Zaki, N. M. Anoop Krishnan. "Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning." Acta materialia, vol. 242, p. 118439, 2023. Pergamon. Link

  13. S. Mannan, M. Zaki, S. Bishnoi, D. R. Cassar, J. Jiusti, J. C. F. Faria, J. F. S. Christensen, N. N. Gosvami, M. M. Smedskjaer, E. D. Zanotto, and others. "Glass hardness: Predicting composition and load effects via symbolic reasoning-informed machine learning." Acta Materialia, vol. 255, p. 119046, 2023. Pergamon. Link

Research Conferences and Workshops

  1. Material Science & Technology (MS&T) 2019 October, 2019
    Venue - Oregon convocation center, Portland, USA
    Talk: Machine learning to predict the elastic properties of glasses
  2. Material Science & Technology (MS&T) 2021 (Online) December, 2021
    Talk: Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses using compositions for large datasets
    Poster: Developing physics-based descriptors for property prediction in oxide glasses
  3. International Materials Research Congress - IMRC 2022 (Online) August, 2022
    Talk: Developing physics-based models for property prediction in oxide glasses using descriptors
  4. International Conference on Learning Representations (ICLR), 2023 May, 2023
    Venue - Kigali convention centre, Rwanda
    Paper: Enhancing the inductive biases of Graph Neural ODE for modeling physical systems
    Workshop (Physics4ML): Learning the dynamics of physical systems with Hamiltonian Graph Neural Networks