most citedPhysics-Informed Neural ODEs with Scale-Aware Residuals for Learning Stiff Biophysical Dynamics

1 citations · 1 across the 6 of their papers we have counts for

collaborators

9 papers

cs.LG2025

Forecasting N-Body Dynamics: A Comparative Study of Neural Ordinary Differential Equations and Universal Differential Equations

Suriya R S, Prathamesh Dinesh Joshi, Rajat Dandekar +2

The n body problem, fundamental to astrophysics, simulates the motion of n bodies acting under the effect of their own mutual gravitational interactions. Traditional machine learni…

cs.LG2025

Adaptive tumor growth forecasting via neural & universal ODEs

Kavya Subramanian, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2

Forecasting tumor growth is critical for optimizing treatment. Classical growth models such as the Gompertz and Bertalanffy equations capture general tumor dynamics but may fail to…

cs.LG20251 cited

Physics-Informed Neural ODEs with Scale-Aware Residuals for Learning Stiff Biophysical Dynamics

Kamalpreet Singh Kainth, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2

Neural differential equations offer a powerful framework for modeling continuous-time dynamics, but forecasting stiff biophysical systems remains unreliable. Standard Neural ODEs a…

q-bio.NC2025

EARS-UDE: Evaluating Auditory Response in Sensory Overload with Universal Differential Equations

Miheer Salunke, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2

Auditory sensory overload affects 50-70% of individuals with Autism Spectrum Disorder (ASD), yet existing approaches, such as mechanistic models (Hodgkin Huxley type, Wilson Cowan,…

cs.LG2025

A study of Universal ODE approaches to predicting soil organic carbon

Satyanarayana Raju G. V., Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2

Soil Organic Carbon (SOC) is a foundation of soil health and global climate resilience, yet its prediction remains difficult because of intricate physical, chemical, and biological…

cs.LG2025

BULL-ODE: Bullwhip Learning with Neural ODEs and Universal Differential Equations under Stochastic Demand

Nachiket N. Naik, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2

We study learning of continuous-time inventory dynamics under stochastic demand and quantify when structure helps or hurts forecasting of the bullwhip effect. BULL-ODE compares a f…