Marimuthu Kalimuthu


Machine Learning for Simulation Science


Universitätsstraße 32
70569 Stuttgart
Room: 2.316

Office Hours

Just drop by my office. Or feel free to send me an email for scheduling an appointment.


Current Topics of Interest:

  • Deep Neural Networks for Solving Partial Differential Equations (PDEs)
  • Efficient Representation Learning for Modeling Dynamical Systems (e.g., PDEs)
  • Physics-Informed Machine Learning (Neural Operators, PINNs, Graph Networks, etc)
  • Machine Learning for Spatio-Temporal Modeling
  • Transformer Models for Vision and Time-Series Data
  • Neural Fields for Learning Physics (e.g., CROM)
  • Generative Models for Computer Vision (Vision Transformers -- Swin, ViT)
  • Graph Neural Networks for Simulations (MPNN, Meshgraphnets, etc)
  • Transfer Learning for Fluid Mechanics

If you're interested in any of the above listed topics for your Bachelor's/Master's Thesis, or Research Project, please feel free to send me an email, attaching your current CV, up-to-date Transcript of Records, and a brief one paragraph motivation.

Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods
Aditya MogadalaMarimuthu KalimuthuDietrich Klakow
JAIR 2021 [arXiv] [Bibtex]

Fusion Models for Improved Image Captioning
Marimuthu KalimuthuAditya MogadalaMarius MosbachDietrich Klakow
ICPR 2021 [arXiv] [Bibtex]

A Competitive Deep Neural Network Approach for the ImageCLEFmed Caption 2020 Task
Marimuthu KalimuthuFabrizio NunnariDaniel Sonntag
ImageCLEF 2020 [arXiv] [Bibtex]

Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings
Marimuthu KalimuthuMichael BarzDaniel Sonntag
ACL 2019 [Bibtex]

Seminar: Deep Learning in the Sciences. Co-organizer along with Mathias Niepert.

Seminar: Machine Learning in the Sciences. Co-organizer along with Mathias Niepert.
Course  : Introduction to Artificial IntelligenceTeaching Assistant.

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