Siddhartha Mishra
Full Professor
Develops machine learning methods for partial differential equations, including neural operators and foundation models for scientific simulation.
Siddhartha Mishra's lab at ETH Zurich works on physics, climate & scientific computing, generative & foundation models and physics-based simulation & animation. 15 papers since 2023, 2 at top venues, mostly TMLR. 11 PhD students. 24 alumni.
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 13%
- PhD students
- 11
- MS students
- 0
- alumni listed
- 24
Papers by yearfrom the lab homepage, 2023–2026
- 2 papers2026 · T1 1 · collecting
- 1 papers2025 · T1 0
- 9 papers2024 · T1 1
- 3 papers2023 · T1 0
T1 venuesOtherVenues: TMLR
Research focuspaper titles that use each topic's words
- Physics-based simulation & animation6 papers
- Physics, climate & scientific computing5 papers
- Generative & foundation models1 paper
- partial0 papers
- partial differential0 papers
Paper list
From the lab homepage.
20262 papers
- PNAS NexusData-driven, ML-assisted approaches to problem well-posedness (opens in a new tab)
- TMLR (T1)Rectified Flows for Fast Multiscale Fluid Flow Modeling
20251 paper
20249 papers
- Geophysical Journal InternationalDeep ensemble geophysics-informed neural networks for the prediction of celestial pole offsets (opens in a new tab)
- Mathematics of ComputationError analysis for deep neural network approximations of parametric hyperbolic conservation laws (opens in a new tab)
- IMA Journal of Numerical AnalysisError estimates for physics-informed neural networks approximating the Navier–Stokes equations (opens in a new tab)
- TMLR (T1)How does over-squashing affect the power of GNNs?
- Computer Methods in Applied Mechanics and EngineeringMultilevel domain decomposition-based architectures for physics-informed neural networks (opens in a new tab)
- Acta NumericaNumerical analysis of physics-informed neural networks and related models in physics-informed machine learning (opens in a new tab)
- SIAM Journal on Mathematical AnalysisOn the Vanishing Viscosity Limit of Statistical Solutions of the Incompressible Navier–Stokes Equations (opens in a new tab)
- Computer Methods in Applied Mechanics and EngineeringThe language of hyperelastic materials (opens in a new tab)
- SIAM Journal on Numerical AnalysiswPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (opens in a new tab)
20233 papers
- Communications on Applied Mathematics and ComputationCurl Constraint-Preserving Reconstruction and the Guidance it Gives for Mimetic Scheme Design (opens in a new tab)
- Journal of Computational PhysicsOn the discrete equation model for compressible multiphase fluid flows (opens in a new tab)
- Computer Methods in Applied Mechanics and EngineeringSingle-track thermal analysis of laser powder bed fusion process: Parametric solution through physics-informed neural networks (opens in a new tab)
Where alumni went24 listed
Apply to ETH ZurichRolling
Not announced yet
Computer Science Doctorate (D-INFK)
- Round
- Rolling (no deadline)
- Opens
- —
- GRE
- See program page
- English
- Proficiency in written and spoken English
- Fee
- See program page
Confirmed on the program page.
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Data
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- Papers
- The lab homepage
- Members
- Lab homepage
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- Lab homepage
Tiers are venue classifications, not lab rankings.