Physics AI Scientist · Professor · UN AI Research Expert Abu Dhabi, UAE
I build physics-informed AI systems that get deployed — not benchmarked and abandoned. My work puts the governing equations inside the network: five families of physics-informed models, built around sound, light, brain signals, continuum flow and the chemistry of the cell.
- Director of Research Projects, Abu Dhabi Maritime Academy — AD Ports Group
- Professor, Kyrgyz National University
- AI Research Expert, United Nations (UNODC)
- Science Advisor, Multiple startups
| Area | What I work on |
|---|---|
| Acoustic PINN | Sound reaches where cameras cannot — under water, inside a machine, through a forest at night (acoustic wave equation, impedance and transmission-loss relations). |
| Spectral PINN | Light carries what the eye misses: water, pigment and structure inside a leaf (radiative transfer equation, Beer–Lambert absorption). |
| Neuro PINN | Electroencephalography shows the brain under real load, where movement drowns the signal (bioelectric field equations, cable equation, artefact models). |
| Navier PINN | Measurements are sparse; the rock, water and air between them are not (Navier–Stokes, Navier–Cauchy, advection–diffusion). |
| Biochem PINN | Yield tells you what happened, chemistry tells you why (Michaelis–Menten kinetics, binding thermodynamics, Farquhar photosynthesis). |
- 116 publications on Google Scholar · 869 citations · h-index 13 · i10-index 15
- ~30 international patents
- 10 books and 3 coursebooks across 4 languages
- Springer volume on PINNs for industrial applications (in progress)
- LinkedIn — https://www.linkedin.com/in/dmitry-mikhaylov
- Substack — https://dmitrymikhaylov.substack.com
- Google Scholar — https://scholar.google.com/citations?user=tfdBDF8AAAAJ
- ORCID — https://orcid.org/0009-0009-2108-6820
- Wikidata — https://www.wikidata.org/wiki/Q88500243