2 citations · 2 across the 2 of their papers we have counts for
5 papers
Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML
Hrishikesh Viswanath, Md Ashiqur Rahman, Abhijeet Vyas +5
Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, and mathematical problems involving func…
Tunable Soft Equivariance with Guarantees
Md Ashiqur Rahman, Lim Jun Hao, Jeremiah Jiang +2
Equivariance is a fundamental property in computer vision models, yet strict equivariance is rarely satisfied in real-world data, which can limit a model's performance. Controlling…
Group Downsampling with Equivariant Anti-aliasing
Md Ashiqur Rahman, Raymond A. Yeh
Downsampling layers are crucial building blocks in CNN architectures, which help to increase the receptive field for learning high-level features and reduce the amount of memory/co…
HessianForge: Scalable LiDAR reconstruction with Physics-Informed Neural Representation and Smoothness Energy Constraints
Hrishikesh Viswanath, Md Ashiqur Rahman, Chi Lin +2
Accurate and efficient 3D mapping of large-scale outdoor environments from LiDAR measurements is a fundamental challenge in robotics, particularly towards ensuring smooth and artif…
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy +9
Existing neural operator architectures face challenges when solving multiphysics problems with coupled partial differential equations (PDEs) due to complex geometries, interactions…