Zobrazeno 1 - 10
of 5 369
pro vyhledávání: '"Raissi, A"'
In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensions, and provide a specific example in data-driven discovery of govern
Externí odkaz:
http://arxiv.org/abs/2408.16806
Graph states are a class of multi-partite entangled quantum states, where colorability, a property rooted in their mathematical foundation, has significant implications for quantum information processing. In this study, we investigate the colorabilit
Externí odkaz:
http://arxiv.org/abs/2408.09515
Non-symmetric GHZ states ($n$-GHZ$_\alpha$), characterized by unequal superpositions of $|00...0>$ and $|11...1>$, represent a significant yet underexplored class of multipartite entangled states with potential applications in quantum information. De
Externí odkaz:
http://arxiv.org/abs/2408.02740
The ongoing research scenario for automatic speech recognition (ASR) envisions a clear division between end-to-end approaches and classic modular systems. Even though a high-level comparison between the two approaches in terms of their requirements a
Externí odkaz:
http://arxiv.org/abs/2407.11641
Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML's "black box" nature has presented a challenge to interpret
Externí odkaz:
http://arxiv.org/abs/2407.10761
This paper introduces DiffMix, a new self-supervised learning (SSL) pre-training framework that combines real and synthetic images. Unlike traditional SSL methods that predominantly use real images, DiffMix uses a variant of Stable Diffusion to repla
Externí odkaz:
http://arxiv.org/abs/2406.12368
The Log-Periodic Power Law Singularity (LPPLS) model offers a general framework for capturing dynamics and predicting transition points in diverse natural and social systems. In this work, we present two calibration techniques for the LPPLS model usi
Externí odkaz:
http://arxiv.org/abs/2405.12803
Artificial neural networks often suffer from catastrophic forgetting, where learning new concepts leads to a complete loss of previously acquired knowledge. We observe that this issue is particularly magnified in vision transformers (ViTs), where pos
Externí odkaz:
http://arxiv.org/abs/2404.17245
3D modeling holds significant importance in the realms of AR/VR and gaming, allowing for both artistic creativity and practical applications. However, the process is often time-consuming and demands a high level of skill. In this paper, we present a
Externí odkaz:
http://arxiv.org/abs/2402.17115
Autor:
Bafghi, Reza Akbarian, Raissi, Maziar
Physics-informed neural networks (PINNs) have gained prominence for their capability to tackle supervised learning tasks that conform to physical laws, notably nonlinear partial differential equations (PDEs). This paper presents "PINNs-TF2", a Python
Externí odkaz:
http://arxiv.org/abs/2311.03626