Zobrazeno 1 - 10
of 57
pro vyhledávání: '"MADDIX, DANIELLE"'
Autor:
Masserano, Luca, Ansari, Abdul Fatir, Han, Boran, Zhang, Xiyuan, Faloutsos, Christos, Mahoney, Michael W., Wilson, Andrew Gordon, Park, Youngsuk, Rangapuram, Syama, Maddix, Danielle C., Wang, Yuyang
How to best develop foundational models for time series forecasting remains an important open question. Tokenization is a crucial consideration in this effort: what is an effective discrete vocabulary for a real-valued sequential input? To address th
Externí odkaz:
http://arxiv.org/abs/2412.05244
Autor:
Cheng, Chaoran, Han, Boran, Maddix, Danielle C., Ansari, Abdul Fatir, Stuart, Andrew, Mahoney, Michael W., Wang, Yuyang
Generative models that satisfy hard constraints are crucial in many scientific and engineering applications where physical laws or system requirements must be strictly respected. However, many existing constrained generative models, especially those
Externí odkaz:
http://arxiv.org/abs/2412.01786
Autor:
Ashton, Neil, Mockett, Charles, Fuchs, Marian, Fliessbach, Louis, Hetmann, Hendrik, Knacke, Thilo, Schonwald, Norbert, Skaperdas, Vangelis, Fotiadis, Grigoris, Walle, Astrid, Hupertz, Burkhard, Maddix, Danielle
Machine Learning (ML) has the potential to revolutionise the field of automotive aerodynamics, enabling split-second flow predictions early in the design process. However, the lack of open-source training data for realistic road cars, using high-fide
Externí odkaz:
http://arxiv.org/abs/2408.11969
The development of Machine Learning (ML) methods for Computational Fluid Dynamics (CFD) is currently limited by the lack of openly available training data. This paper presents a new open-source dataset comprising of high fidelity, scale-resolving CFD
Externí odkaz:
http://arxiv.org/abs/2407.20801
Autor:
Ashton, Neil, Angel, Jordan B., Ghate, Aditya S., Kenway, Gaetan K. W., Wong, Man Long, Kiris, Cetin, Walle, Astrid, Maddix, Danielle C., Page, Gary
This paper presents a new open-source high-fidelity dataset for Machine Learning (ML) containing 355 geometric variants of the Windsor body, to help the development and testing of ML surrogate models for external automotive aerodynamics. Each Computa
Externí odkaz:
http://arxiv.org/abs/2407.19320
Autor:
Karlbauer, Matthias, Maddix, Danielle C., Ansari, Abdul Fatir, Han, Boran, Gupta, Gaurav, Wang, Yuyang, Stuart, Andrew, Mahoney, Michael W.
Remarkable progress in the development of Deep Learning Weather Prediction (DLWP) models positions them to become competitive with traditional numerical weather prediction (NWP) models. Indeed, a wide number of DLWP architectures -- based on various
Externí odkaz:
http://arxiv.org/abs/2407.14129
Autor:
Qiu, Shikai, Han, Boran, Maddix, Danielle C., Zhang, Shuai, Wang, Yuyang, Wilson, Andrew Gordon
How do we transfer the relevant knowledge from ever larger foundation models into small, task-specific downstream models that can run at much lower costs? Standard transfer learning using pre-trained weights as the initialization transfers limited in
Externí odkaz:
http://arxiv.org/abs/2406.07337
Autor:
Mouli, S. Chandra, Maddix, Danielle C., Alizadeh, Shima, Gupta, Gaurav, Stuart, Andrew, Mahoney, Michael W., Wang, Yuyang
Existing work in scientific machine learning (SciML) has shown that data-driven learning of solution operators can provide a fast approximate alternative to classical numerical partial differential equation (PDE) solvers. Of these, Neural Operators (
Externí odkaz:
http://arxiv.org/abs/2403.10642
Autor:
Ansari, Abdul Fatir, Stella, Lorenzo, Turkmen, Caner, Zhang, Xiyuan, Mercado, Pedro, Shen, Huibin, Shchur, Oleksandr, Rangapuram, Syama Sundar, Arango, Sebastian Pineda, Kapoor, Shubham, Zschiegner, Jasper, Maddix, Danielle C., Wang, Hao, Mahoney, Michael W., Torkkola, Kari, Wilson, Andrew Gordon, Bohlke-Schneider, Michael, Wang, Yuyang
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model a
Externí odkaz:
http://arxiv.org/abs/2403.07815
Autor:
Gao, Zhihan, Shi, Xingjian, Han, Boran, Wang, Hao, Jin, Xiaoyong, Maddix, Danielle, Zhu, Yi, Li, Mu, Wang, Yuyang
Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enab
Externí odkaz:
http://arxiv.org/abs/2307.10422