Google's DeepMind uncovered surprising solutions to the equations governing fluid dynamics, potentially accelerating vehicle ...

Before founding Amazon, Jeff Bezos aspired to be a theoretical physicist at Princeton. A challenging homework problem, solved instantly by a friend, made him realize physics was not his calling, ...

[1] Kailai Xu, Bella Shi, Shuyi Yin. 2018. Deep learning for Partial Differential Equations (PDEs). CS230. [2] Maziar Raissi, Paris Perdikaris, and George Em Karniadakis. Physics informed deep ...

Abstract: We develop a framework for estimating unknown partial differential equations (PDEs) from noisy data, using a deep learning approach. Given noisy samples of a solution to an unknown PDE, our ...

Abstract: Frequency dynamics during primary frequency control is closely related to pre-disturbance operating conditions, the disturbance, and equipment characteristics. How to rigorously express such ...

Ask the publishers to restore access to 500,000+ books. An icon used to represent a menu that can be toggled by interacting with this icon. A line drawing of the Internet Archive headquarters building ...

The report, Seizing Quantum’s Edge: Why and How HPC Should Prepare for eFTQC, presents a clear conclusion: the HPC community must act now to prepare for the arrival of early fault-tolerant quantum ...

The oldest mathematics journal in the Western Hemisphere in continuous publication, the American Journal of Mathematics ranks as one of the most respected and celebrated journals in its field.

We also prove that the two sets of Maxwell equations only depend on the non-linear elations of the conformal group of ...

[1] M. Raissi, P. Perdikaris, and G. E. Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential ...