5 papers
APIC: Amortized Physics-Informed Calibration using Neural Processes
Aishwarya Venkataramanan, Sai Karthikeya Vemuri, Joachim Denzler
Physics models are inherently imperfect due to misspecified or missing mechanisms, resulting in systematic discrepancies between model predictions and real-world observations. The…
RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics
Sai Karthikeya Vemuri, Adithya Ashok Chalain Valapil, Tim Büchner +1
Transferring the recent advancements in deep learning into scientific disciplines is hindered by the lack of the required large-scale datasets for training. We argue that in these…
F-INR: Functional Tensor Decomposition for Implicit Neural Representations
Sai Karthikeya Vemuri, Tim Büchner, Joachim Denzler
Implicit Neural Representations (INRs) model signals as continuous, differentiable functions. However, monolithic INRs scale poorly with data dimensionality, leading to excessive t…
Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction
Aishwarya Venkataramanan, Sai Karthikeya Vemuri, Adithya Ashok Chalain Valapil +1
Coherent anti-Stokes Raman scattering (CARS) spectroscopy is a powerful and rapid technique widely used in medicine, material science, and chemical analyses. However, its effective…
Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks
Phillip Rothenbeck, Sai Karthikeya Vemuri, Niklas Penzel +1
The COVID-19 pandemic has highlighted the need for quantitative modeling and analysis to understand real-world disease dynamics. In particular, post hoc analyses using compartmenta…