Zobrazeno 1 - 10
of 200
pro vyhledávání: '"Wu, Dongxia"'
Black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle. This process demands sample-efficient optimization due to the high computational cost of function evaluations. While prior studies focus o
Externí odkaz:
http://arxiv.org/abs/2407.00610
Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian processes can hardly scale to high-dimensional data. Deep learning ap
Externí odkaz:
http://arxiv.org/abs/2402.18846
Autor:
Kong, Lingkai, Du, Yuanqi, Mu, Wenhao, Neklyudov, Kirill, De Bortoli, Valentin, Wang, Haorui, Wu, Dongxia, Ferber, Aaron, Ma, Yi-An, Gomes, Carla P., Zhang, Chao
Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed the issue of unknown objectives, limited research has focused on scen
Externí odkaz:
http://arxiv.org/abs/2402.18012
Current generative models for drug discovery primarily use molecular docking to evaluate the quality of generated compounds. However, such models are often not useful in practice because even compounds with high docking scores do not consistently sho
Externí odkaz:
http://arxiv.org/abs/2402.10387
Autor:
Wu, Dongxia, Idé, Tsuyoshi, Lozano, Aurélie, Kollias, Georgios, Navrátil, Jiří, Abe, Naoki, Ma, Yi-An, Yu, Rose
We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level causal structures in an unsupervised manner. Instance-level causality i
Externí odkaz:
http://arxiv.org/abs/2402.03726
To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highes
Externí odkaz:
http://arxiv.org/abs/2305.04392
Science and engineering fields use computer simulation extensively. These simulations are often run at multiple levels of sophistication to balance accuracy and efficiency. Multi-fidelity surrogate modeling reduces the computational cost by fusing di
Externí odkaz:
http://arxiv.org/abs/2206.04872
Publikováno v:
In Applied Thermal Engineering 15 July 2024 249
Autor:
Hou, Jiancai, Hu, Jiangliang, Cui, Xinmin, Du, Shasha, Wu, Dongxia, Chang, Liping, Wang, Sheng, Bao, Weiren, Wang, Jiancheng
Publikováno v:
In Fuel 15 July 2024 368
Publikováno v:
In Energy Conversion and Management 15 May 2024 308