5 papers
Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO Carbon Monoxide Sensor with p-n Switching
Sani Biswas, Preetam Singh, Amit Kumar Gangwar
This work presents a physics-guided machine-learning framework for carbon monoxide concentration inference from experimentally measured resistance transients of a mixed-phase SnO-S…
A Coupled Physics-Informed Neural Network for Greenhouse Climate State Reconstruction and Parameter Identification under Sparse Sensor Measurements
Sani Biswas, Khursheed J. Ansari, Md. Nasim Akhtar
Accurate reconstruction of greenhouse climate variables from sparse sensor measurements is essential for intelligent environmental monitoring, automated climate control, and precis…
A Randomized Milstein Scheme for SDEs with Superlinear Drift Coefficient
Sani Biswas
This work presents a randomized-tamed Milstein scheme for stochastic differential equations whose drift coefficient exhibits superlinear growth in the state variable and limited te…
An Explicit Euler-type Scheme for Lévy-driven SDEs with Superlinear and Time-Irregular Coefficients
Sani Biswas, Joaquin Fontbona
This paper introduces a randomized tamed Euler scheme tailored for Lévy-driven stochastic differential equations (SDEs) with superlinear random coefficients and Carathéodory-type…
Milstein-type schemes for McKean-Vlasov SDEs driven by Brownian motion and Poisson random measure (with super-linear coefficients)
Sani Biswas, Chaman Kumar, Christoph Reisinger +1
In this work, we present a general Milstein-type scheme for McKean-Vlasov stochastic differential equations (SDEs) driven by Brownian motion and Poisson random measure and the asso…