Research
Few selected publications are given below. You can find the latest list of publications in my Google Scholar page.
2026
Learning Physical Operators using Neural Operators
OpsSplit introduces a physics-informed machine learning framework that decomposes partial differential equations into fixed linear approximations and learned non-linear neural operators within a Neural ODE, delivering superior generalization to unseen physics, parameter efficiency, and interpretability.
2025
Calibrated Physics-Informed Uncertainty Quantification
Calibrated uncertainty quantification of neural PDE solvers using physics residual errors as non-conformity scores for data-free conformal prediction.
2024
Uncertainty Quantification of Surrogate Models using Conformal Prediction
Guaranteed and valid error bars across spatio-temporal domains using conformal prediction.
Valid Error Bars for Neural Weather Models using Conformal Prediction
Marginal conformal prediction as a method of guaranteed error bars across neural weather models.
Plasma Surrogate Modelling using Fourier Neural Operators
Multi-variable FNO designed to model the plasma evolution within a Tokamak across both simulations and experiment on the MAST Tokamak.
2023
Fourier-RNNs for Modelling Noisy Physics Data
Recurrent Fourier neural operators with hidden state representations for non-Markovian physical modelling.
Loss Landscape Engineering via Data Regulation on PINNs
Impact Data-Regulation has on smoothening the loss landscape of physics-informed neural networks for better convergence.
2022
