A multidimensional and multiscale model for pressure analysis in a reservoir-pipe-valve system

Reservoir-pipe-valve (RPV) systems are widely used in many industrial processes. The pressure in an RPV system plays an important role in the safe operation of the system, especially during the sudden operations such as rapid valve opening or closing. To investigate the pressure response, with particular interest in the pressure fluctuations in an RPV system, a multidimensional and multiscale model combining the method of characteristics (MOC) and computational fluid dynamics (CFD) method is proposed. In the model, the reservoir is modeled as a zero-dimensional virtual point, the pipe is modeled as a one-dimensional system using the MOC, and the valve is modeled using a threedimensional CFD model. An interface model is used to connect the multidimensional and multiscale model. Based on the model, a transient simulation of the turbulent flow in an RPV system is conducted in which not only the pressure fluctuation in the pipe but also the detailed pressure distribution in the valve is obtained. The results show that the proposed model is in good agreement when compared with a high fidelity CFD model used to represent both large-scale and small-scale spaces. As expected, the proposed model is significantly more computationally efficient than the CFD model. This demonstrates the feasibility of analyzing complex RPV systems within an affordable computational time

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA

Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CP v2 dataset introduces a distribution shift between the training and test set given a question type. In this way, the model cannot use the training set shortcut (from question type to answer) to perform well on the test set. However, VQA-CP v2 only considers one type of shortcut and thus still cannot guarantee that the model relies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, we overcome the three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. Our benchmark provides a more rigorous and comprehensive testbed for shortcut learning in VQA. We benchmark recent methods and find that methods specifically designed for particular shortcuts fail to simultaneously generalize to our varying OOD test sets. We also systematically study the varying shortcuts and provide several valuable findings, which may promote the exploration of shortcut learning in VQA.Comment: Fingdings of EMNLP-202

Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators

Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples