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Higher-order interactions shape collective dynamics, but how they affect transitions between different states in swarmalator systems is yet to be determined. To that effect, we here study an analytically tractable swarmalator model that incorporates both
We formulate physics-informed neural networks (PINNs) for full-field reconstruction of rotational flow beneath nonlinear periodic water waves using a small
possible but lies outside of the scope of this paper. Discussion and conclusions In this work, we demonstrated how a collocation point-based technique can improve the performance of an emerging class of continuous-time physics-informed neural-network based reduced-order models. First, we ...
Fig. 1. Sample of MWCS items. Contrariwise, VASS made up of 33 items, does not have any correct answer; thus, items are rated on a 5-point scale, and each scale an ‘a’ revealing a negative attitude and a ‘b’ statement revealing a positive attitude (see Fig. 2). The first sc...
2 shows the performance and the solutions found by the agent during the training time. The agent learns how to approximate the target after about 104 episodes, while improving the solution over time. At the end of the learning, the agent discovered an approximating circuit made by 76 gates ...
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(FNO) and its variants, e.g., the physics-informed FNO, to encode both the initial and boundary conditions as part of the inputs and to learn the nonlinear traffic-related operator directly, in other words, mapping (𝑥,𝑡)(x,t) to the solutions associated with the given initial and...