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1. S7: Selective and Simplified State Space Layers for Sequence Modeling

2. Generative AI for fast and accurate Statistical Computation of Fluids

3. Poseidon: Efficient Foundation Models for PDEs

4. FUSE: Fast Unified Simulation and Estimation for PDEs

6. SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models

8. Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning

9. The Language of Hyperelastic Materials

10. Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows

11. A universal approximation theorem for nonlinear resistive networks

13. An operator preconditioning perspective on training in physics-informed machine learning

14. Multilevel domain decomposition-based architectures for physics-informed neural networks

15. How does over-squashing affect the power of GNNs?

16. Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning

17. Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains

18. Neural Oscillators are Universal

20. A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows

21. A Survey on Oversmoothing in Graph Neural Networks

22. Multi-Scale Message Passing Neural PDE Solvers

23. Convolutional Neural Operators for robust and accurate learning of PDEs

24. Neural Inverse Operators for Solving PDE Inverse Problems

25. Finite basis physics-informed neural networks as a Schwarz domain decomposition method

26. Finite Basis Physics-Informed Neural Networks as a Schwarz Domain Decomposition Method

27. Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

28. Gradient Gating for Deep Multi-Rate Learning on Graphs

30. wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws

31. Error analysis for deep neural network approximations of parametric hyperbolic conservation laws

32. Agnostic Physics-Driven Deep Learning

33. Variable-Input Deep Operator Networks

34. Generic bounds on the approximation error for physics-informed (and) operator learning

35. Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

36. On the discrete equation model for compressible multiphase fluid flows

37. Error estimates for physics informed neural networks approximating the Navier-Stokes equations

38. Graph-Coupled Oscillator Networks

41. A novel fourth-order WENO interpolation technique. A possible new tool designed for radiative transfer

42. Long Expressive Memory for Sequence Modeling

43. On the vanishing viscosity limit of statistical solutions of the incompressible Navier-Stokes equations

46. Well-posedness of Bayesian inverse problems for hyperbolic conservation laws

47. On Bayesian data assimilation for PDEs with ill-posed forward problems

48. On universal approximation and error bounds for Fourier Neural Operators

49. Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs

50. Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings

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