Conference: Support for Early Career Participants in Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP)

2022年机器学习集成物理建模不确定性量化大会(UQ-MLIP)将是一个汇聚美国各地不确定性量化(UQ)和机器学习(ML)物理建模领域的顶尖专家、科学家和年轻研究人员的场所，目的是：(1)确定推动该领域发展的挑战和机会；(2)在一个地方快速传播最新进展；以及(C)为早期职业研究人员提供联网和指导机会。会议将于2022年8月18日至19日在弗吉尼亚州阿灵顿举行，由UQ和ML物理建模领域的领先专家小组组织。这笔赠款将资助学生和一小部分早期职业研究人员参加会议，深入参与UQ-MLIP研究社区。学生和早期职业研究人员将参加小组讨论，参加受邀的讲座，并参加海报会议或发表简短的演讲。会议还将倡导在美国计算力学协会(USACM)不确定性量化技术推力领域的支持下，开发共享数据集，供共享社区用于验证和算法评估。正如NSF的使命所述，该项目符合国家利益，促进科学进步，因为它提供了一个传播研究成果、联系研究人员和培训下一代学者的论坛。会议组织者正在从不同的机构招募希望参与该项目的学生和早期职业研究人员，并以不同的参与者为目标。未来的参与者必须提交申请，提交的申请由一个委员会评估科学价值和促进多样性。学生和早期职业研究人员将参加职业小组讨论，参加受邀的技术讲座，并参加海报讨论会或就他们的研究发表简短的演讲。NSF提供的资金将对未来一代研究人员在不确定性量化、机器学习和物理建模方面的职业生涯产生重大影响，同时鼓励该领域的多样性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The 2022 Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP) will be a venue for bringing together leading experts, scientists, and young researchers from across the US, in the domains of uncertainty quantification (UQ) and machine learning (ML) for physics modeling in order to: (1) identify challenges and opportunities to advance the field; (2) rapidly disseminate the latest advances in a single place; and (C) provide networking and mentoring opportunities for early career researchers. The conference will be held in Arlington, Virginia, August 18-19, 2022, and is organized by a leading group of experts in UQ and ML for physics modeling. This grant will fund students and a small number of early career researchers to participate in the conference, to engage deeply with the UQ-MLIP research community. The students and early career researchers will participate in panel discussions, attend invited talk sessions, and participate in poster sessions or present short talks. The conference will also advocate for the development of shared datasets for shared community use for validation and algorithm evaluation, under the auspices of the USACM (U.S. Association for Computational Mechanics) technical thrust area of uncertainty quantification. The project serves the national interest, as stated by NSF's mission, to promote the progress of science as it provides a forum to disseminate research efforts, connect researchers, and train the next generation of scholars. The conference organizers are recruiting students and early career researchers wishing to participate in the program from a diverse set of institutions, and with the goal of a diverse set of participants. Prospective participants will have to submit an application, and submissions are evaluated by a committee for scientific merit and to promote diversity. Students and early career researchers will participate in career panel discussions, attend invited technical talk sessions, and participate in poster sessions or present short talks on their research. The funding provided by NSF will have a significant impact on the careers of the future generation of researchers in uncertainty quantification, machine learning, and physics modeling, while encouraging diversity in the field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.