Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Raissi, Maziar"'
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
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
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
The inclusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physical constraints and adhering to physical laws. In this work we explore
Externí odkaz:
http://arxiv.org/abs/2309.01909
Autor:
Yazdani, Mahdieh, Raissi, Maziar
The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vis
Externí odkaz:
http://arxiv.org/abs/2302.00117
Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations in a forward and inverse manner using deep neural networks. However, training these networks can be challenging for multiscale problems. While stat
Externí odkaz:
http://arxiv.org/abs/2301.13262
Autor:
Raissi, Maziar
This work formulates the machine learning mechanism as a bi-level optimization problem. The inner level optimization loop entails minimizing a properly chosen loss function evaluated on the training data. This is nothing but the well-studied training
Externí odkaz:
http://arxiv.org/abs/2301.11316