A Continuous hp−hp-Mesh Model for Discontinuous Petrov-Galerkin Finite Element Schemes with Optimal Test Functions

We present an anisotropic $hp-$mesh adaptation strategy using a continuous mesh model for discontinuous Petrov-Galerkin (DPG) finite element schemes with optimal test functions, extending our previous work on $h-$adaptation. The proposed strategy utilizes the inbuilt residual-based error estimator of the DPG discretization to compute both the polynomial distribution and the anisotropy of the mesh elements. In order to predict the optimal order of approximation, we solve local problems on element patches, thus making these computations highly parallelizable. The continuous mesh model is formulated either with respect to the error in the solution, measured in a suitable norm, or with respect to certain admissible target functionals. We demonstrate the performance of the proposed strategy using several numerical examples on triangular grids. Keywords: Discontinuous Petrov-Galerkin, Continuous mesh models, $hp-$ adaptations, Anisotrop

An Anisotropic hphp-Adaptation Framework for Ultraweak Discontinuous Petrov-Galerkin Formulations

In this article, we present a three-dimensional anisotropic $hp$-mesh refinement strategy for ultraweak discontinuous Petrov--Galerkin (DPG) formulations with optimal test functions. The refinement strategy utilizes the built-in residual-based error estimator accompanying the DPG discretization. The refinement strategy is a two-step process: (a) use the built-in error estimator to mark and isotropically $hp$-refine elements of the (coarse) mesh to generate a finer mesh; (b) use the reference solution on the finer mesh to compute optimal $h$- and $p$-refinements of the selected elements in the coarse mesh. The process is repeated with coarse and fine mesh being generated in every adaptation cycle, until a prescribed error tolerance is achieved. We demonstrate the performance of the proposed refinement strategy using several numerical examples on hexahedral meshes

Engineering Method of Prediction of Plume Path of Air Launched Missile

Separation dynamics study of an air-launched missile is a paramount task for ensuring the safety of launch aircraft. The study should certify that there is absolute absence of any physical interference of missile with the aircraft at any circumstance. It is also important to ensure that the interference of rocket motor plume of hot-launched missile does not have any significant effect on the structure, on board electronic components and sensitive parts of the aircraft. The plume ingestion into the aircraft intake is a critical problem which endangers the safety of the aircraft. Therefore, the prediction of plume path of hot-launched missile is a significant part of separation dynamics study. An engineering approach based on a particle tracking method was followed in predicting the plume path in the present work. Further, the method is modified using reverse particle tracking method to make it more efficient. The method is applied in predicting plume path for an air-to air-missile and is found that this approach gives reasonably accurate plume path with minimum computational requirements

Separation Dynamics of Air-to-Air Missile and Validation with Flight Data

Prediction of flight characteristics of a store in the vicinity of an aircraft is vitally important for ensuring the safety of the aircraft and effectiveness of the store to meet the mission objective. Separation dynamics of an agile air-to-air-Missile from a fighter aircraft is numerically simulated using an integrated store separation dynamics suite. Chimera cloud of points along with a grid-free Euler solver is used to obtain aerodynamic force on the missile and the force is integrated using a rigid body dynamics code to obtain the missile position. In the present work, the suite is applied to a flight test case and sensitivity of trajectory variables on launch parameters is studied. Further, the results of the suite are compared with the flight data. The predicted body rates and Euler angles of missile compare well with the flight data. 

Correlation Power Analysis Attack against STT-MRAM Based Cyptosystems

Emerging technologies such as Spin-transfer torque magnetic random-access memory (STT-MRAM) are considered potential candidates for implementing low-power, high density storage systems. The vulnerability of such nonvolatile memory (NVM) based cryptosystems to standard side-channel attacks must be thoroughly assessed before deploying them in practice. In this paper, we outline a generic Correlation Power Analysis (CPA) attack strategy against STT-MRAM based cryptographic designs using a new power model. In our proposed attack methodology, an adversary exploits the power consumption patterns during the write operation of an STT-MRAM based cryptographic implementation to successfully retrieve the secret key. In order to validate our proposed attack technique, we mounted a CPA attack on MICKEY-128 2.0 stream cipher design consisting of STT-MRAM cells with Magnetic Tunnel Junctions (MTJs) as storage elements. The results of the experiments show that the STT-MRAM based implementation of the cipher circuit is susceptible to standard differential power analysis attack strategy provided a suitable hypothetical power model (such as the one proposed in this paper) is selected. In addition, we also investigated the effectiveness of state-of-the-art side-channel attack countermeasures for MRAMs and found that our proposed scheme is able to break such protected implementations as well

A Residual-Based HP-Mesh Optimization Technique for Petrov-Galerkin Schemes with Optimal Test Functions

In recent times, Petrov-Galerkin schemes with optimal test function framework have presented themselves as a stable and robust technique for solving partial differential equations. These schemes are also accompanied by an inbuilt error estimator, which makes them an ideal candidate for mesh adaptation. In this paper, we present a metric-based mesh adaptation strategy utilizing this inbuilt error estimator to generate optimal hp meshes

Hardware-Assisted Intellectual Property Protection of Deep Learning Models

The protection of intellectual property (IP) rights of well-trained deep learning (DL) models has become a matter of major concern, especially with the growing trend of deployment of Machine Learning as a Service (MLaaS). In this work, we demonstrate the utilization of a hardware root-of-trust to safeguard the IPs of such DL models which potential attackers have access to. We propose an obfuscation framework called Hardware Protected Neural Network (HPNN) in which a deep neural network is trained as a function of a secret key and then, the obfuscated DL model is hosted on a public model sharing platform. This framework ensures that only an authorized end-user who possesses a trustworthy hardware device (with the secret key embedded on-chip) is able to run intended DL applications using the published model. Extensive experimental evaluations show that any unauthorized usage of such obfuscated DL models result in significant accuracy drops ranging from 73.22 to 80.17% across different neural network architectures and benchmark datasets. In addition, we also demonstrate the robustness of proposed HPNN framework against a model fine-tuning type of attack