Donya Dabiri

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Education

• M.Sc. in Mechanical Engineering - Energy Conversion (2020), University of Tehran
• B.Sc. in Mechanical Engineering (2018), K. N. Toosi University of Technology
  

Donya Dabiri is a PhD candidate with a strong background in numerical methods, including computational fluid dynamics (CFD) and Lattice Boltzmann method (LBM) alongside implementing them through object-oriented programming in C++ and Python. Donya is now working on physics-informed neural networks (PINNs) and her research can be summarized in two categories:

1. Fractional physics-informed neural networks (fPINNs):
   • Using fPINNs in fractional constitutive models of viscoelastic fluids in order to recover their rheological properties and fractional parameters.
   • Using fPINNs to solve fractional equations, including fractional ODEs (fODE), two and three dimensional fractional PDEs (fPDE).
2. Model discovery from scarce and noisy data:
   • Applying sparse regression in PINNs in order to unravel governing equations from scarce and noisy data.

Donya is highly passionate about a variety of fields related to machine learning and is open to collaborations and innovative ideas. Feel free to reach out!