Satyasaran Changdar

Satyasaran Changdar

Postdoc


Publication year:
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1 - 6 out of 6Page size: 10
  1. 2024
  2. Published

    An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters

    Saha, S., De, S. & Changdar, Satyasaran, 2024, In: Journal of Offshore Mechanics and Arctic Engineering. 146, 1, 12 p., 011202.

    Research output: Contribution to journal › Journal article › Research › peer-review

  3. Published

    Deep learning based solution of nonlinear partial differential equations arising in the process of arterial blood flow

    Bhaumik, B., De, S. & Changdar, Satyasaran, 2024, In: Mathematics and Computers in Simulation. 217, p. 21-36

    Research output: Contribution to journal › Journal article › Research › peer-review

  4. Published

    Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet

    Pandey, Sumit, Changdar, Satyasaran, Perslev, Mathias & Dam, Erik Bjørnager, 2024, arxiv.org, 9 p.

    Research output: Working paper › Preprint › Research

  5. Published

    Nanoparticle aggregation and electro-osmotic propulsion in peristaltic transport of third-grade nanofluids through porous tube

    Dolui, S., Bhaumik, B., De, S. & Changdar, Satyasaran, 2024, In: Computers in Biology and Medicine. 176, 20 p., 108617.

    Research output: Contribution to journal › Journal article › Research › peer-review

  6. E-pub ahead of print

    Physics informed machine learning based applications for the stability analysis of breakwaters

    Saha, S., De, S. & Changdar, Satyasaran, 2024, (E-pub ahead of print) In: Ships and Offshore Structures. p. 1-13

    Research output: Contribution to journal › Journal article › Research › peer-review

  7. E-pub ahead of print

    Prediction of the stability number of conventional rubble-mound breakwaters using machine learning algorithms

    Saha, S., Changdar, Satyasaran & De, S., 2024, (E-pub ahead of print) In: Journal of Ocean Engineering and Science.

    Research output: Contribution to journal › Journal article › Research › peer-review

ID: 283968692