68 citations · 107 across the 14 of their papers we have counts for
17 papers
Strongly isotropy irreducible spaces whose Laplacians have all their eigenspaces irreducible
Emilio A. Lauret, Fiorela Rossi Bertone
Let be a closed subgroup of a compact connected Lie group such that is simply connected, the action of on is almost effective, and is a strongly isotr…
Sim+Real: Joint Simulation-Experiment Training Improves Balanced Prediction in Physical Systems
Mahindra Rautela, Alexander Scheinker, Ayan Biswas +3
Simulation and experimental measurements provide complementary data for learning spatiotemporal physical systems, but standard simulation-to-experiment fine-tuning optimizes only t…
PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion
Mahindra Rautela, Alexander Scheinker, Bradley Love +4
PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific data. However, most downstrea…
Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading
Mahindra Rautela, Alexander Most, Siddharth Mansingh +9
Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains unclear. We benchmark out-of-dis…
Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems
Mahindra Rautela, Alexander Scheinker
Virtual beam diagnostics relies on computationally intensive beam dynamics simulations where high-dimensional charged particle beams evolve through the accelerator. We propose Late…
MORPH: PDE Foundation Models with Arbitrary Data Modality
Mahindra Singh Rautela, Alexander Most, Siddharth Mansingh +6
We introduce MORPH, a modality-agnostic, autoregressive foundation model for partial differential equations (PDEs). MORPH is built on a convolutional vision transformer backbone th…
Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space
Mahindra Rautela, Alan Williams, Alexander Scheinker
Complex dynamical systems, such as particle accelerators, require tuning over high-dimensional, nonlinear state and parameter spaces while experimental measurements and high-fideli…
Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal
Mahindra Rautela, Alan Williams, Alexander Scheinker
Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high performance physics-based simulators for predicting behav…
Towards latent space evolution of spatiotemporal dynamics of six-dimensional phase space of charged particle beams
Mahindra Rautela, Alan Williams, Alexander Scheinker
Addressing the charged particle beam diagnostics in accelerators poses a formidable challenge, demanding high-fidelity simulations in limited computational time. Machine learning (…
Accelerator system parameter estimation using variational autoencoded latent regression
Mahindra Rautela, Alan Williams, Alexander Scheinker
Particle accelerators are time-varying systems whose components are perturbed by external disturbances. Tuning accelerators can be a time-consuming process involving manual adjustm…
A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators
Mahindra Rautela, Alan Williams, Alexander Scheinker
Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to lim…
Bayesian optimized physics-informed neural network for estimating wave propagation velocities
Mahindra Rautela, S. Gopalakrishnan, J. Senthilnath
In this paper, we propose a novel inverse parameter estimation approach called Bayesian optimized physics-informed neural network (BOPINN). In this study, a PINN solves the partial…
Deep generative models for unsupervised delamination detection using guided waves
Mahindra Rautela, Amin Maghareh, Shirley Dyke +1
With the rising demands for robust structural health monitoring procedures for aerospace structures, the scope of intelligent algorithms and learning techniques is expanding. Super…
Towards deep generation of guided wave representations for composite materials
Mahindra Rautela, J. Senthilnath, Armin Huber +1
Laminated composite materials are widely used in most fields of engineering. Wave propagation analysis plays an essential role in understanding the short-duration transient respons…
Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search
Mahindra Rautela, Motahareh Mirfarah, Christian Silva +3
The recent accelerated growth in space-related research and development activities makes the near-term need for long-term extraterrestrial habitats evident. Such habitats must oper…
Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion
Mahindra Rautela, Armin Huber, J. Senthilnath +1
In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup s…
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations
Mahindra Rautela, J. Senthilnath, Ernesto Monaco +1
With the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for aerospace composite struct…