arXiv:2606. 12025v1 Announce Type: new Abstract: Finite element (FE) modeling of safety-critical infrastructure such as bridge barriers requires high-fidelity nonlinear dynamic analysis, yet the current FE modeling process remains labor-intensive and lacks automation.
By Quankai Wang, Yulin Xie, Tongfei Yang, Minghui Cheng, Ran Cao
arXiv:2608.21976v1 Announce Type: new
Abstract: Agentic AI has automated parts of scientific discovery, including paper generation, expert-level coding, therapeutic proposal, and autonomous experimen...
By Tianyi Yu, Chengxing Tao, Haoxuan Shen, Huiyang Li, Rugang Chen, Long Teng, Lilin Wang, Yan Li, Qingbin Chen, Chaogang Xu, Lizhong Wang
arXiv:2606. 01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes and adapting to evolving requirements.
By Ruiyin Li, Yiran Zhang, Xiyu Zhou, Yangxiao Cai, Peng Liang, Weisong Sun, Jifeng Xuan, Zhi Jin, Yang Liu
arXiv:2606. 00138v1 Announce Type: new Abstract: Finite element analysis (FEA) is the most important numerical approach for solid mechanics.
By Titu Ranjan Sarker, Muhammed Jawaad Zulqernine, Ling Yue, Shaowu Pan, Chenxi Wang, Shiyao Lin
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focused primarily on code implementation and execution, overlooking the importance of this stage, and no benchmark exists to evaluate AI's ability to conduct systematic experiment design.
arXiv:2606. 17577v1 Announce Type: new Abstract: AI-driven engineering workflows face particular challenges in crash safety design: unlike aerodynamics, crash events involve highly nonlinear contact dynamics, material nonlinearity, and discrete state transitions that are difficult to capture with data-driven surrogate models.
By Osamu Ito, Akihiko Katagiri, Yoshikazu Nakagawa, Shin Saeki, Jun Shiraishi, Masato Sasaki