1 citations · 2 across the 11 of their papers we have counts for
12 papers
PRISM-UDE: Physics-Regularized Iterative Symbolic Modeling of 3nm FinFETs via Universal Differential Equation
Pranavanath Balamurali, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2
Compact transistor models are the mathematical backbone of circuit simulation. However, at advanced nodes such as 3nm, transport physics becomes too complex for traditional hand-de…
SciML in the Wild: A Diagnostic Study of When Structural Priors Help and When They Hurt
Vrishank Sai Anand, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2
Scientific Machine Learning (SciML) methods such as Neural Ordinary Differential Equations (NODEs), Physics-Informed Neural Networks (PINNs), and Universal Differential Equations (…
Three methods, one problem: Classical and AI approaches to no-three-in-line
Pranav Ramanathan, Thomas Prellberg, Matthew Lewis +4
The No-Three-In-Line problem asks for the maximum number of points that can be placed on an n by n grid with no three collinear, representing a famous problem in combinatorial geom…
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…
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…
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…
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,…
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…
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…
Understanding Malware Propagation Dynamics through Scientific Machine Learning
Karthik Pappu, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2
Accurately modeling malware propagation is essential for designing effective cybersecurity defenses, particularly against adaptive threats that evolve in real time. While tradition…
Physical Informed Neural Networks for modeling ocean pollutant
Karishma Battina, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2
Traditional numerical methods often struggle with the complexity and scale of modeling pollutant transport across vast and dynamic oceanic domains. This paper introduces a Physics-…
HULLMI: Human vs LLM identification with explainability
Prathamesh Dinesh Joshi, Sahil Pocker, Raj Abhijit Dandekar +2
As LLMs become increasingly proficient at producing human-like responses, there has been a rise of academic and industrial pursuits dedicated to flagging a given piece of text as "…