Publikationen von Navyanth Kusampudi

Kusampudi, N.; Diehl, M.: Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization. International Journal of Plasticity 171, 103776 (2023)

Saxena, A.; Polin, N.; Kusampudi, N.; Katnagallu, S.; Molina-Luna, L.; Gutfleisch, O.; Berkels, B.; Gault, B.; Neugebauer, J.; Freysoldt, C.: A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Data. Microscopy and Microanalysis 29 (5), S. 1658 - 1670 (2023)

Raabe, D.; Sun, B.; Kwiatkowski da Silva, A.; Gault, B.; Yen, H.-W.; Sedighiani, K.; Prithiv, T. S.; Souza Filho, I. R.; Katnagallu, S.; Jägle, E. A. et al.; Kürnsteiner, P.; Kusampudi, N.; Stephenson, L.; Herbig, M.; Liebscher, C.; Springer, H.; Zaefferer, S.; Shah, V.; Wong, S. L.; Baron, C.; Diehl, M.; Roters, F.; Ponge, D.: Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels. Metallurgical and Materials Transactions A 51, S. 5517 - 5586 (2020)

Kusampudi, N.; Diehl, M.: Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization. Working Group Microstructural Mechanics, Deutsche Gesellschaft für Materialkunde e.V., Applications of Machine Learning for Mechanical Behavior of Materials, Online (2022)

Diehl, M.; Kusampudi, N.: Using machine learning and crystal plasticity simulation to design damage resistant dual phase steels. Webinar: Metal Plasticity Seminar - Artificial Intelligence, Machine Learning and Big Data in Metal Plasticity, Leuven, Belgium (2021)

Diehl, M.; Kusampudi, N.; Kusche, C.; Raabe, D.; Korte-Kerzel, S.: Combining Experiments, Simulations, and Data Science to Understand Damage in Dual Phase Steels. International Conference on Plasticity, Damage, and Fracture, Riviera May, Mexico (2020)

Kusampudi, N.: Application of topological data analysis and sliding window embedding to analyze time series data from bird songs. Danish-Swedish summer school on TDA and spatial statistics, Aalborg, Sweden (2023)

Kusampudi, N.: Using Machine Learning and Data-driven Approaches to Predict Damage Initiation in Dual-Phase Steels. Master, Ruhr-Universität Bochum (2020)