3 papers
cs.ET2026
Closed-Form Optimal Weight Splitting for Matrix-Vector Multiply on Noisy In-Memory Accelerators
Mehmet Aktukmak, David A. Roberts
Analog in-memory computing performs the matrix-vector multiplications of neural networks directly inside the memory array, which saves time and energy. But each memory cell stores…
math.NA2025
Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications
K. Adhikari, Md. Lal Mamud, M. K. Mudunuru +1
This study presents a physics-informed neural network (PINN) framework for reactive transport modeling for simulating fast bimolecular reactions in porous media. Accurate character…
physics.flu-dyn2023
Quantifying local and global mass balance errors in physics-informed neural networks
Md Lal Mamud, Maruti K. Mudunuru, Satish Karra +1
Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss fu…