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会议（UQ-MLIP）将汇集来自美国各地的领先专家，科学家和年轻研究人员，在不确定性量化（UQ）和机器学习（ML）领域进行物理建模，以便：（1）确定推进该领域的挑战和机遇;（2）在一个地方迅速传播最新进展;（C）为早期职业研究人员提供网络和指导机会。 该会议将于2022年8月18日至19日在弗吉尼亚州阿灵顿举行，由UQ和ML的物理建模专家领导小组组织。 这笔赠款将资助学生和少数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，参加邀请谈话会议，并参加海报会议或发表简短的演讲。会议还将倡导在USACM（美国计算力学协会）不确定性量化技术重点领域的主持下，开发共享数据集，供共享社区用于验证和算法评估。该项目服务于国家利益，正如NSF的使命所述，促进科学的进步，因为它提供了一个论坛，以传播研究成果，连接研究人员，并培养下一代学者。会议组织者正在招募学生和早期职业研究人员希望从不同的机构参与该计划，并与不同的参与者的目标。未来的参与者必须提交申请，并由一个委员会对提交的申请进行科学价值和促进多样性的评估。学生和早期职业研究人员将参加职业小组讨论，参加邀请的技术讲座，并参加海报会议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领域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知识价值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.