Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser's Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. The present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.Comment: 28 pages, 18 figures, 4 table

All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management

During the deployment of deep neural networks (DNNs) on edge devices, many research efforts are devoted to the limited hardware resource. However, little attention is paid to the influence of dynamic power management. As edge devices typically only have a budget of energy with batteries (rather than almost unlimited energy support on servers or workstations), their dynamic power management often changes the execution frequency as in the widely-used dynamic voltage and frequency scaling (DVFS) technique. This leads to highly unstable inference speed performance, especially for computation-intensive DNN models, which can harm user experience and waste hardware resources. We firstly identify this problem and then propose All-in-One, a highly representative pruning framework to work with dynamic power management using DVFS. The framework can use only one set of model weights and soft masks (together with other auxiliary parameters of negligible storage) to represent multiple models of various pruning ratios. By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i.e., keeping the difference in speed performance under various execution frequencies as small as possible. Our experiments demonstrate that our method not only achieves high accuracy for multiple models of different pruning ratios, but also reduces their variance of inference latency for various frequencies, with minimal memory consumption of only one model and one soft mask

Use the Spear as a Shield: A Novel Adversarial Example based Privacy-Preserving Technique against Membership Inference Attacks

Recently, the membership inference attack poses a serious threat to the privacy of confidential training data of machine learning models. This paper proposes a novel adversarial example based privacy-preserving technique (AEPPT), which adds the crafted adversarial perturbations to the prediction of the target model to mislead the adversary's membership inference model. The added adversarial perturbations do not affect the accuracy of target model, but can prevent the adversary from inferring whether a specific data is in the training set of the target model. Since AEPPT only modifies the original output of the target model, the proposed method is general and does not require modifying or retraining the target model. Experimental results show that the proposed method can reduce the inference accuracy and precision of the membership inference model to 50%, which is close to a random guess. Further, for those adaptive attacks where the adversary knows the defense mechanism, the proposed AEPPT is also demonstrated to be effective. Compared with the state-of-the-art defense methods, the proposed defense can significantly degrade the accuracy and precision of membership inference attacks to 50% (i.e., the same as a random guess) while the performance and utility of the target model will not be affected

Non-Darcy displacement by a non-Newtonian fluid in porous media according to the Barree-Conway model

An analytical solution for describing the non-Darcy displacement of a Newtonian fluid by a non-Newtonian fluid in porous media has been developed. The two-phase non-Darcy flow is described using the Barree-Conway model under multi- phase conditions. A power-law non-Newtonian fluid, whose viscosity is a function of the flow potential gradient and the phase saturation, is considered. The analytical solution is similar to the Buckley-Leverett theoretical solution, which can be regarded as an extension of the Buckley-Leverett theory to the non-Darcy flow of non-Newtonian fluids. The analytical results revel how non-Darcy displacement by a non-Newtonian fluid is controlled not only by relative permeabilities but also by non-Darcy flow coefficients as well as non-Newtonian rheological constitutive parameters and injection rates. The comparison among Darcy, Forchheimer and Barree-Conway models is also discussed. For application, the analytical solution is then applied to verify a numerical simulator for modeling multi-phase non-Darcy flow of non-Newtonian fluids.Cited as: Huang, Z., Zhang, X., Yao, J., et al. Non-Darcy displacement by a non-Newtonian fluid in porous media according to the Barree-Conway model. Advances in Geo-Energy Research, 2017, 1(2): 74-85, doi: 10.26804/ager.2017.02.0

Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search

Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices. To overcome the challenge and facilitate the real-time deployment of SISR tasks on mobile, we combine neural architecture search with pruning search and propose an automatic search framework that derives sparse super-resolution (SR) models with high image quality while satisfying the real-time inference requirement. To decrease the search cost, we leverage the weight sharing strategy by introducing a supernet and decouple the search problem into three stages, including supernet construction, compiler-aware architecture and pruning search, and compiler-aware pruning ratio search. With the proposed framework, we are the first to achieve real-time SR inference (with only tens of milliseconds per frame) for implementing 720p resolution with competitive image quality (in terms of PSNR and SSIM) on mobile platforms (Samsung Galaxy S20)

Modeling Particle Gel Propagation in Porous Media

Gel treatments are a proven cost-effective method to reduce excess water production and improve sweep efficiency in waterflood reservoirs. A newer trend in gel treatments uses particle gel (PG) to overcome some distinct drawbacks inherent in in-situ gelation systems. In this paper, we present a conceptual numerical model, based on laboratory tests and analyses, to simulate PG propagation through porous rock. In particular, we use a continuum modeling approach to simulate PG movement and its impact on isothermal oil and water flow and displacement processes. In this conceptual model, the PG is treated as one additional component to the water phase. This simplified treatment is based on the following physical considerations: (1) PG is mobilized only within the aqueous phase by advection in reservoirs; (2) PG, once retained in the porous media, will occupy pore space in pore bodies or pore throats and therefore reduce the permeability to bypassing water or oil; and (3) PG mobilization may not occur through pores or pore throats until some thresholds in pressure and/or pressure gradients are achieved and these threshold conditions are described by analogy to non-Newtonian fluid or non-Darcy flow in porous media, i.e., by a modified Darcy\u27s law. The model is able to predict and evaluate the effects of PG as a conformance control agent to improve oil production and control excess water production