Upcoming/Preprints
2026
[P2] T. Ramgopal, N.H.Gowtham, H. Farahani, C. Bos, S. Kumar, Data-efficient continuous conditional denoising diffusion model for microstructure generation, ArXiv:2607.10429.
[P1] Robin C. Laurence, Ranggi Sahmura Ramadhan, Sandra Cabeza, Arnold Paecklar, Thilo Pirling, Philipp Mayr, Ricardo Fernandez, Alec E. Davis, N. H. Gowtham, Zhe Cai, Philip J. Withers, and Matthew J. Roy, Assessing the limits of the residual stress determination by diffraction in aluminium castings, Manuscript submitted to International Journal of Metalcasting (under review)
N.H.Gowtham, J T Jegadeesan, Michael D White, Chris P Race, Philip J Withers, Bikramjit Basu. Exploring StyleGAN2-ADA for titanium alloy microstructure generation: A study on data scarcity and augmentations. Manuscript under review in xxxx.
PEER-REVIEWED PUBLICATIONS
2025
[J8] N.H.Gowtham, J T Jegadeesan, RVS Prasad, Bikramjit Basu., Machine learning analysis for melt pool geometry prediction of direct energy deposited SS316L single tracks. Journal of Materials Science, Vol. 60, pp. 1477-1503 (2025). DOI: 10.1007/s10853-024-10276-5
2024
[J7] N.H.Gowtham, David Canelo-Yubero, Emad Maawad, Guilherme Abreu Faria, Peter Staron, Norbert Schell, Ranggi Sahmura Ramadhan, Sandra Cabeza, Arnold Paecklar, Thilo Pirling, Philip J. Withers, Matthew J. Roy, Benchmark Sample Design for the Validation of Residual Stress Measurements by Diffraction: Insights and Practicalities. Integrated Materials Manufacturing Innovation, Vol. 13, pp. 955-968 (2024). DOI: 10.1007/s40192-024-00385-z
[J6] Michael D White, N.H. Gowtham, J T Jegadeesan, Bikramjit Basu, Philip J Withers, Chris P Race, . Exploring descriptors for titanium microstructure via digital fingerprints from variational autoencoders. Computational Materials Science, Vol. 240, Article 112992 (2024). DOI: 10.1016/j.commatsci.2024.112992
[J5] N.H.Gowtham, Bikramjit Basu, Implementing machine learning approaches for accelerated prediction of biomechanical response in acetabulum of a hip joint. Journal of Mechanical Behavior of Biomedical Materials, Vol. 153:106495 (2024). DOI: 10.1016/j.jmbbm.2024.106495
2023
[J4] N.H.Gowtham, J T Jegadeesan, Chiranjib Bhattacharya, Bikramjit Basu, A deep adversarial approach for the generation of synthetic titanium alloy microstructures with limited training data. Computational Materials Science, Vol. 230, 112512 (2023). DOI: 10.1016/j.commatsci.2023.112512
[J3] Srimanta Barui, Deepa Mishra, N.H.Gowtham, Bikramjit Basu, No more ‘core-shell’ in binderjetting of bioceramics: Novel solution and experimental validation in microstructure and mechanical properties. Journal European Ceramic Society, Vol. 43(3), 1178-1188 (2023). DOI: 10.1016/j.jeurceramsoc.2022.10.055
2022
[J2] Bikramjit Basu, N.H.Gowtham, Yang Xiao, Surya R Kalidindi, Kam W Leong, Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials. Acta Biomaterialia, Vol. 143, 1-25 (2022). DOI: 10.1016/j.actbio.2022.02.027
[J1] Nihal Kottan, N.H.Gowtham, Bikramjit Basu. Development and Validation of a Finite Element Model of Wear in UHMWPE Liner Using Experimental Data From Hip Simulator Studies. ASME Journal of Biomechanical Engineering, Vol. 144(3), 031001 (2022). DOI: 10.1115/1.4052373
BOOK CHAPTERS
[B2] Vidushi Sharma, N.H.Gowtham, Biswanath Kundu, Vamsi Krishna Balla, D.C. Sundaresh, Bikramjit Basu. Unraveling Translational Research on Ultra-High Molecular Weight Polyethylene in Total Hip Joint Arthroplasty: A Lab-to-Industry-Scale Pilot Study. In Emerging Materials and Technologies for Bone Repair and Regeneration, CRC Press, 2024, pp. 1–26. eBook ISBN: 9781003307310. Link
[B1] Rakesh Pemmada, N.H.Gowtham, Yiyun Xia, Bikramjit Basu, Vinoy Thomas. ML and AI approaches for design of tissue scaffolds. In Artificial Intelligence in Tissue and Organ Regeneration, Academic Press, 2023, pp. 29–56. ISBN: 9780443184987. DOI: 10.1016/B978-0-443-18498-7.00008-9. Link