5 citations · 10 across the 9 of their papers we have counts for
9 papers
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20
Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Ray…
Breakeven complexity: A new perspective on neural partial differential equation solvers
Yijing Zhang, Nicholas Roberts, Tanya Marwah +1
Neural surrogate solvers of partial differential equations (PDEs) promise dramatic speedups over numerical methods, especially in scenarios requiring many solves. However, current…
CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
Shanda Li, Tanya Marwah, Junhong Shen +4
Partial differential equations (PDEs) are fundamental to modeling physical systems, yet solving them remains a complex challenge. Traditional numerical solvers rely on expert knowl…
Towards characterizing the value of edge embeddings in Graph Neural Networks
Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton +3
Graph neural networks (GNNs) are the dominant approach to solving machine learning problems defined over graphs. Despite much theoretical and empirical work in recent years, our un…
On the Benefits of Memory for Modeling Time-Dependent PDEs
Ricardo Buitrago Ruiz, Tanya Marwah, Albert Gu +1
Data-driven techniques have emerged as a promising alternative to traditional numerical methods for solving PDEs. For time-dependent PDEs, many approaches are Markovian -- the evol…
Deep Equilibrium Based Neural Operators for Steady-State PDEs
Tanya Marwah, Ashwini Pokle, J. Zico Kolter +3
Data-driven machine learning approaches are being increasingly used to solve partial differential equations (PDEs). They have shown particularly striking successes when training an…
Disentangling the Mechanisms Behind Implicit Regularization in SGD
Zachary Novack, Simran Kaur, Tanya Marwah +2
A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD)leads to improved generalization over the full-batch regime, with re…
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
Tanya Marwah, Zachary C. Lipton, Jianfeng Lu +1
A burgeoning line of research leverages deep neural networks to approximate the solutions to high dimensional PDEs, opening lines of theoretical inquiry focused on explaining how i…
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
Tanya Marwah, Zachary C. Lipton, Andrej Risteski
Recent experiments have shown that deep networks can approximate solutions to high-dimensional PDEs, seemingly escaping the curse of dimensionality. However, questions regarding th…