Engineering Sciences

148 items

Scientific Reports Sep 25, 2026
Using historical general user proposals from three beamlines at Oak Ridge National Laboratory's Spallation Neutron Source (EQ-SANS, CNCS, POWGEN), the study has large language models judge every pair of proposals within a run cycle, converts the win-lose outcomes into rankings with the Bradley-Terry model, and finds that LLM rankings correlate positively with human rankings (Spearman ρ about 0.2-0.8, rising to ≥0.5 after 10% outlier removal), show no statistically significant difference from human review in identifying proposals with high publication potential, cost over two orders of magnitude less, and support linear-complexity proposal similarity analysis via embedding models.
Using historical general user proposals from three beamlines at Oak Ridge National Laboratory's Spallation Neutron Source (EQ-SANS, CNCS, POWGEN), the study has large language models judge every pair of proposals within a run cycle, converts the win-lose outcomes into rankings with the Bradley-Terry model, and finds that LLM rankings correlate positively with human rankings (Spearman ρ about 0.2-0.8, rising to ≥0.5 after 10% outlier removal), show no statistically significant difference from human review in identifying proposals with high publication potential, cost over two orders of magnitude less, and support linear-complexity proposal similarity analysis via embedding models.
LLM pairwise comparison of historical proposals at three Spallation Neutron Source beamlines ranks them in positive correlation with human ranking, matches human reviewers at flagging high-publication-potential proposals, and costs over two orders of magnitude less
Using historical general user proposals from three beamlines at Oak Ridge National Laboratory's Spallation Neutron Source (EQ-SANS, CNCS, POWGEN), the study has large language models judge every pair of proposals within a run cycle, converts the win-lose outcomes into rankings with the Bradley-Terry model, and finds that LLM rankings correlate positively with human rankings (Spearman ρ about 0.2-0.8, rising to ≥0.5 after 10% outlier removal), show no statistically significant difference from human review in identifying proposals with high publication potential, cost over two orders of magnitude less, and support linear-complexity proposal similarity analysis via embedding models.
Using historical general user proposals from three beamlines at Oak Ridge National Laboratory's Spallation Neutron Source (EQ-SANS, CNCS, POWGEN), the study has large language models judge every pair of proposals within a run cycle, converts the win-lose outcomes into rankings with the Bradley-Terry model, and finds that LLM rankings correlate positively with human rankings (Spearman ρ about 0.2-0.8, rising to ≥0.5 after 10% outlier removal), show no statistically significant difference from human review in identifying proposals with high publication potential, cost over two orders of magnitude less, and support linear-complexity proposal similarity analysis via embedding models.
SIAM Journal on Scientific Computing Sep 25, 2026
The authors develop and evaluate a data-free, physics-informed method for learning solution operators: after a finite element discretization, a multilayer perceptron (MLP) maps problem data parameters (boundary conditions, coefficients, right-hand sides, etc.) to the finite element degrees of freedom, with a loss function given by the expected energy functional, plus a parallelizable training algorithm that can use random batches of mesh elements; on a parametric Poisson problem, a Gaussian random field coefficient problem, and a nonlinear neo-Hookean elastic beam, learning errors are small (e.g., for the parametric problem with the full mesh and a 4·512 network, mean relative energy error 2.0e-4, L2 0.0020, H1 0.
The authors develop and evaluate a data-free, physics-informed method for learning solution operators: after a finite element discretization, a multilayer perceptron (MLP) maps problem data parameters (boundary conditions, coefficients, right-hand sides, etc.) to the finite element degrees of freedom, with a loss function given by the expected energy functional, plus a parallelizable training algorithm that can use random batches of mesh elements; on a parametric Poisson problem, a Gaussian random field coefficient problem, and a nonlinear neo-Hookean elastic beam, learning errors are small (e.g., for the parametric problem with the full mesh and a 4·512 network, mean relative energy error 2.0e-4, L2 0.0020, H1 0.
Learning nonlinear finite element solution operators with MLPs and energy minimization yields small learning errors on parametric Poisson, Gaussian random field, and nonlinear elasticity cases, and speeds up Newton's method when the network prediction is used as an initial guess
The authors develop and evaluate a data-free, physics-informed method for learning solution operators: after a finite element discretization, a multilayer perceptron (MLP) maps problem data parameters (boundary conditions, coefficients, right-hand sides, etc.) to the finite element degrees of freedom, with a loss function given by the expected energy functional, plus a parallelizable training algorithm that can use random batches of mesh elements; on a parametric Poisson problem, a Gaussian random field coefficient problem, and a nonlinear neo-Hookean elastic beam, learning errors are small (e.g., for the parametric problem with the full mesh and a 4·512 network, mean relative energy error 2.0e-4, L2 0.0020, H1 0.
The authors develop and evaluate a data-free, physics-informed method for learning solution operators: after a finite element discretization, a multilayer perceptron (MLP) maps problem data parameters (boundary conditions, coefficients, right-hand sides, etc.) to the finite element degrees of freedom, with a loss function given by the expected energy functional, plus a parallelizable training algorithm that can use random batches of mesh elements; on a parametric Poisson problem, a Gaussian random field coefficient problem, and a nonlinear neo-Hookean elastic beam, learning errors are small (e.g., for the parametric problem with the full mesh and a 4·512 network, mean relative energy error 2.0e-4, L2 0.0020, H1 0.
SIAM Journal on Scientific Computing Sep 25, 2026
This work systematically derives a family of algorithms for the LTLT (L unit lower triangular, T skew-symmetric tridiagonal) triangular tridiagonalization of a skew-symmetric matrix X using the FLAME methodology, presents unpivoted and pivoted blocked right-looking, left-looking, and fused variants, identifies new level-2 and level-3 BLAS-like operations, and implements them with BLIS 2.0 packing mechanisms and OpenMP parallelism; experiments show the best implementations greatly exceed the performance of the only known prior software, PFAPACK and Pfaffine, while matching or exceeding related symmetric factorization software.
This work systematically derives a family of algorithms for the LTLT (L unit lower triangular, T skew-symmetric tridiagonal) triangular tridiagonalization of a skew-symmetric matrix X using the FLAME methodology, presents unpivoted and pivoted blocked right-looking, left-looking, and fused variants, identifies new level-2 and level-3 BLAS-like operations, and implements them with BLIS 2.0 packing mechanisms and OpenMP parallelism; experiments show the best implementations greatly exceed the performance of the only known prior software, PFAPACK and Pfaffine, while matching or exceeding related symmetric factorization software.
FLAME-derived LTLT factorization algorithms for skew-symmetric matrices, with fused BLAS-like operations, greatly outperform PFAPACK and Pfaffine
This work systematically derives a family of algorithms for the LTLT (L unit lower triangular, T skew-symmetric tridiagonal) triangular tridiagonalization of a skew-symmetric matrix X using the FLAME methodology, presents unpivoted and pivoted blocked right-looking, left-looking, and fused variants, identifies new level-2 and level-3 BLAS-like operations, and implements them with BLIS 2.0 packing mechanisms and OpenMP parallelism; experiments show the best implementations greatly exceed the performance of the only known prior software, PFAPACK and Pfaffine, while matching or exceeding related symmetric factorization software.
This work systematically derives a family of algorithms for the LTLT (L unit lower triangular, T skew-symmetric tridiagonal) triangular tridiagonalization of a skew-symmetric matrix X using the FLAME methodology, presents unpivoted and pivoted blocked right-looking, left-looking, and fused variants, identifies new level-2 and level-3 BLAS-like operations, and implements them with BLIS 2.0 packing mechanisms and OpenMP parallelism; experiments show the best implementations greatly exceed the performance of the only known prior software, PFAPACK and Pfaffine, while matching or exceeding related symmetric factorization software.
IEEE Spectrum Sep 25, 2026
A 15-person multidisciplinary team from ITESO, Universidad Jesuita de Guadalajara (engineering students, faculty advisors, and IEEE Guadalajara Section volunteers) developed RoboMeshA through the EPICS in IEEE initiative, a portable all-in-one educational platform that brings robotics, computer vision, and AI experiences into high school classrooms lacking robotics laboratories; students connect via a web browser to manually interact with the robot or use its control modes to watch it move and detect and avoid obstacles, the team has built two units and partnered with CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings, and it is developing a modular coupling framework so that four RoboMeshA robots can operate together.
A 15-person multidisciplinary team from ITESO, Universidad Jesuita de Guadalajara (engineering students, faculty advisors, and IEEE Guadalajara Section volunteers) developed RoboMeshA through the EPICS in IEEE initiative, a portable all-in-one educational platform that brings robotics, computer vision, and AI experiences into high school classrooms lacking robotics laboratories; students connect via a web browser to manually interact with the robot or use its control modes to watch it move and detect and avoid obstacles, the team has built two units and partnered with CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings, and it is developing a modular coupling framework so that four RoboMeshA robots can operate together.
Mexico ITESO team builds portable robotics teaching platform RoboMeshA under EPICS in IEEE, with two units built and classroom validation at high schools
A 15-person multidisciplinary team from ITESO, Universidad Jesuita de Guadalajara (engineering students, faculty advisors, and IEEE Guadalajara Section volunteers) developed RoboMeshA through the EPICS in IEEE initiative, a portable all-in-one educational platform that brings robotics, computer vision, and AI experiences into high school classrooms lacking robotics laboratories; students connect via a web browser to manually interact with the robot or use its control modes to watch it move and detect and avoid obstacles, the team has built two units and partnered with CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings, and it is developing a modular coupling framework so that four RoboMeshA robots can operate together.
A 15-person multidisciplinary team from ITESO, Universidad Jesuita de Guadalajara (engineering students, faculty advisors, and IEEE Guadalajara Section volunteers) developed RoboMeshA through the EPICS in IEEE initiative, a portable all-in-one educational platform that brings robotics, computer vision, and AI experiences into high school classrooms lacking robotics laboratories; students connect via a web browser to manually interact with the robot or use its control modes to watch it move and detect and avoid obstacles, the team has built two units and partnered with CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings, and it is developing a modular coupling framework so that four RoboMeshA robots can operate together.
arXiv Sep 24, 2026
Using Fisher information and random matrix theory, the authors derive a closed-form expression for the noise-induced error of chaotic/diffusive reconstructive spectrometers, linking the variance bound σ²_ε Tr[(AᵀA)⁺] to the spectral correlation length Γ_corr, mean transmittance T₀, and the numbers of frequency channels N and measurement channels M, establish conditions for super-resolution, and validate the theory with a random matrix model and full-wave FDTD simulations, where the predicted optimal on-chip cavity size is about 17.9 µm.
Using Fisher information and random matrix theory, the authors derive a closed-form expression for the noise-induced error of chaotic/diffusive reconstructive spectrometers, linking the variance bound σ²_ε Tr[(AᵀA)⁺] to the spectral correlation length Γ_corr, mean transmittance T₀, and the numbers of frequency channels N and measurement channels M, establish conditions for super-resolution, and validate the theory with a random matrix model and full-wave FDTD simulations, where the predicted optimal on-chip cavity size is about 17.9 µm.
Random matrix theory yields a closed-form noise-error bound for reconstructive spectrometers and predicts a 17.9 µm optimal cavity in on-chip FDTD
Using Fisher information and random matrix theory, the authors derive a closed-form expression for the noise-induced error of chaotic/diffusive reconstructive spectrometers, linking the variance bound σ²_ε Tr[(AᵀA)⁺] to the spectral correlation length Γ_corr, mean transmittance T₀, and the numbers of frequency channels N and measurement channels M, establish conditions for super-resolution, and validate the theory with a random matrix model and full-wave FDTD simulations, where the predicted optimal on-chip cavity size is about 17.9 µm.
Using Fisher information and random matrix theory, the authors derive a closed-form expression for the noise-induced error of chaotic/diffusive reconstructive spectrometers, linking the variance bound σ²_ε Tr[(AᵀA)⁺] to the spectral correlation length Γ_corr, mean transmittance T₀, and the numbers of frequency channels N and measurement channels M, establish conditions for super-resolution, and validate the theory with a random matrix model and full-wave FDTD simulations, where the predicted optimal on-chip cavity size is about 17.9 µm.
ACM Transactions on Software Engineering and Methodology Sep 24, 2026
This study empirically evaluates 7 representative diffusion LLMs (including the closed-source Mercury-Coder-Small) against 4 open-source autoregressive baselines on HumanEval/HumanEval+, MBPP/MBPP+, HumanEval-X, LiveCodeBench, RepoQA, RepoBench-C, and SWE-Bench Verified, finding promising but uneven code-generation ability: the closed-source diffusion model leads on most benchmarks (e.g., 79.1% on MBPP+ versus 73.
This study empirically evaluates 7 representative diffusion LLMs (including the closed-source Mercury-Coder-Small) against 4 open-source autoregressive baselines on HumanEval/HumanEval+, MBPP/MBPP+, HumanEval-X, LiveCodeBench, RepoQA, RepoBench-C, and SWE-Bench Verified, finding promising but uneven code-generation ability: the closed-source diffusion model leads on most benchmarks (e.g., 79.1% on MBPP+ versus 73.
First systematic empirical study of diffusion LLMs for code generation: 7 diffusion models reach 79.1% on MBPP+ versus 73.3% for the best autoregressive baseline, yet open-source diffusion models still do not consistently match strong autoregressive models
This study empirically evaluates 7 representative diffusion LLMs (including the closed-source Mercury-Coder-Small) against 4 open-source autoregressive baselines on HumanEval/HumanEval+, MBPP/MBPP+, HumanEval-X, LiveCodeBench, RepoQA, RepoBench-C, and SWE-Bench Verified, finding promising but uneven code-generation ability: the closed-source diffusion model leads on most benchmarks (e.g., 79.1% on MBPP+ versus 73.
This study empirically evaluates 7 representative diffusion LLMs (including the closed-source Mercury-Coder-Small) against 4 open-source autoregressive baselines on HumanEval/HumanEval+, MBPP/MBPP+, HumanEval-X, LiveCodeBench, RepoQA, RepoBench-C, and SWE-Bench Verified, finding promising but uneven code-generation ability: the closed-source diffusion model leads on most benchmarks (e.g., 79.1% on MBPP+ versus 73.
ACM Transactions on Design Automation of Electronic Systems Sep 24, 2026
The work proposes PrefixAgent, a two-phase large-language-model-driven framework for prefix adder optimization: in Phase I a fine-tuned large reasoning model iteratively optimizes the backbone via regroup tool calls, and in Phase II it performs local timing repair with level-opt, fanout-opt, and node clone tools; the authors use e-graph equality saturation and explanation to generate interpretable rewrite trajectories as supervision data, and under the NanGate45 and OpenROAD flow PrefixAgent produces smaller-area adders than DP, MCTS, PrefixRL, CircuitVAE, and PrefixGPT in nearly all configurations, achieving up to 11.3% area reduction over the best baseline at 64 bits and also improving area in a commercial flow and a 256-PE systolic array.
The work proposes PrefixAgent, a two-phase large-language-model-driven framework for prefix adder optimization: in Phase I a fine-tuned large reasoning model iteratively optimizes the backbone via regroup tool calls, and in Phase II it performs local timing repair with level-opt, fanout-opt, and node clone tools; the authors use e-graph equality saturation and explanation to generate interpretable rewrite trajectories as supervision data, and under the NanGate45 and OpenROAD flow PrefixAgent produces smaller-area adders than DP, MCTS, PrefixRL, CircuitVAE, and PrefixGPT in nearly all configurations, achieving up to 11.3% area reduction over the best baseline at 64 bits and also improving area in a commercial flow and a 256-PE systolic array.
PrefixAgent uses a two-phase LLM agent with e-graph trajectory fine-tuning to cut 64-bit prefix adder area to 938 µm², 11.3% below the strongest baseline
The work proposes PrefixAgent, a two-phase large-language-model-driven framework for prefix adder optimization: in Phase I a fine-tuned large reasoning model iteratively optimizes the backbone via regroup tool calls, and in Phase II it performs local timing repair with level-opt, fanout-opt, and node clone tools; the authors use e-graph equality saturation and explanation to generate interpretable rewrite trajectories as supervision data, and under the NanGate45 and OpenROAD flow PrefixAgent produces smaller-area adders than DP, MCTS, PrefixRL, CircuitVAE, and PrefixGPT in nearly all configurations, achieving up to 11.3% area reduction over the best baseline at 64 bits and also improving area in a commercial flow and a 256-PE systolic array.
The work proposes PrefixAgent, a two-phase large-language-model-driven framework for prefix adder optimization: in Phase I a fine-tuned large reasoning model iteratively optimizes the backbone via regroup tool calls, and in Phase II it performs local timing repair with level-opt, fanout-opt, and node clone tools; the authors use e-graph equality saturation and explanation to generate interpretable rewrite trajectories as supervision data, and under the NanGate45 and OpenROAD flow PrefixAgent produces smaller-area adders than DP, MCTS, PrefixRL, CircuitVAE, and PrefixGPT in nearly all configurations, achieving up to 11.3% area reduction over the best baseline at 64 bits and also improving area in a commercial flow and a 256-PE systolic array.
NVIDIA Technical Blog Sep 24, 2026
NVIDIA released NVCRE (Cluster Readiness Engine), an open-source Kubernetes controller that runs real distributed workloads (NCCL communication, DCGM level-4 diagnostics, NeMo Nemotron 5 pretraining), measures results across topology-aware node groups, and reports exactly which nodes failed and why, verifying GPU cluster readiness before production workloads land instead of hand-writing NCCL manifests and bisecting racks manually.
NVIDIA released NVCRE (Cluster Readiness Engine), an open-source Kubernetes controller that runs real distributed workloads (NCCL communication, DCGM level-4 diagnostics, NeMo Nemotron 5 pretraining), measures results across topology-aware node groups, and reports exactly which nodes failed and why, verifying GPU cluster readiness before production workloads land instead of hand-writing NCCL manifests and bisecting racks manually.
NVIDIA open-sources NVCRE: running real distributed workloads on Kubernetes turns pre-512-GPU-training cluster readiness into a provable property
NVIDIA released NVCRE (Cluster Readiness Engine), an open-source Kubernetes controller that runs real distributed workloads (NCCL communication, DCGM level-4 diagnostics, NeMo Nemotron 5 pretraining), measures results across topology-aware node groups, and reports exactly which nodes failed and why, verifying GPU cluster readiness before production workloads land instead of hand-writing NCCL manifests and bisecting racks manually.
NVIDIA released NVCRE (Cluster Readiness Engine), an open-source Kubernetes controller that runs real distributed workloads (NCCL communication, DCGM level-4 diagnostics, NeMo Nemotron 5 pretraining), measures results across topology-aware node groups, and reports exactly which nodes failed and why, verifying GPU cluster readiness before production workloads land instead of hand-writing NCCL manifests and bisecting racks manually.
Microsoft Research Sep 24, 2026
This work systematically measures mobile robotic manipulation workloads (semantic mapping and planning, navigation, and manipulation) across onboard, edge, and cloud GPU configurations, reporting that offloading inference off the robot improves task performance and battery lifetime, and releases Kubernetes-based automatic offloading tooling as a new capability in the Physical AI Toolchain.
This work systematically measures mobile robotic manipulation workloads (semantic mapping and planning, navigation, and manipulation) across onboard, edge, and cloud GPU configurations, reporting that offloading inference off the robot improves task performance and battery lifetime, and releases Kubernetes-based automatic offloading tooling as a new capability in the Physical AI Toolchain.
Offloading Physical AI Inference from the Robot to Edge or Cloud GPUs: A Systematic Measurement Study of Mobile Manipulation Workloads
This work systematically measures mobile robotic manipulation workloads (semantic mapping and planning, navigation, and manipulation) across onboard, edge, and cloud GPU configurations, reporting that offloading inference off the robot improves task performance and battery lifetime, and releases Kubernetes-based automatic offloading tooling as a new capability in the Physical AI Toolchain.
This work systematically measures mobile robotic manipulation workloads (semantic mapping and planning, navigation, and manipulation) across onboard, edge, and cloud GPU configurations, reporting that offloading inference off the robot improves task performance and battery lifetime, and releases Kubernetes-based automatic offloading tooling as a new capability in the Physical AI Toolchain.
Angewandte Chemie International Edition Sep 23, 2026
Using atomistic simulations with an ab initio-quality machine learning potential, this work investigates aqueous LiMn2O4 (LMO) interfaces across varying lithiation states and shows that local Li content determines surface Mn oxidation states and governs interfacial acid-base chemistry, identifies the O-H stretching band as a sensitive spectroscopic probe of the surface electronic structure whose oxidation-state-dependent shifts reveal that the mixed-valence spinel surface hosts coexisting Lewis-acidic centers and neighboring oxygen sites susceptible to electrophilic attack, extending the conventional acid-centric view of Mn dissolution toward a dual-site mechanism.
Using atomistic simulations with an ab initio-quality machine learning potential, this work investigates aqueous LiMn2O4 (LMO) interfaces across varying lithiation states and shows that local Li content determines surface Mn oxidation states and governs interfacial acid-base chemistry, identifies the O-H stretching band as a sensitive spectroscopic probe of the surface electronic structure whose oxidation-state-dependent shifts reveal that the mixed-valence spinel surface hosts coexisting Lewis-acidic centers and neighboring oxygen sites susceptible to electrophilic attack, extending the conventional acid-centric view of Mn dissolution toward a dual-site mechanism.
Machine-learning-potential simulations show that local Li content sets Mn oxidation states at LiMn2O4 surfaces and that the O-H stretch band exposes a dual-site acid-attack vulnerability
Using atomistic simulations with an ab initio-quality machine learning potential, this work investigates aqueous LiMn2O4 (LMO) interfaces across varying lithiation states and shows that local Li content determines surface Mn oxidation states and governs interfacial acid-base chemistry, identifies the O-H stretching band as a sensitive spectroscopic probe of the surface electronic structure whose oxidation-state-dependent shifts reveal that the mixed-valence spinel surface hosts coexisting Lewis-acidic centers and neighboring oxygen sites susceptible to electrophilic attack, extending the conventional acid-centric view of Mn dissolution toward a dual-site mechanism.
Using atomistic simulations with an ab initio-quality machine learning potential, this work investigates aqueous LiMn2O4 (LMO) interfaces across varying lithiation states and shows that local Li content determines surface Mn oxidation states and governs interfacial acid-base chemistry, identifies the O-H stretching band as a sensitive spectroscopic probe of the surface electronic structure whose oxidation-state-dependent shifts reveal that the mixed-valence spinel surface hosts coexisting Lewis-acidic centers and neighboring oxygen sites susceptible to electrophilic attack, extending the conventional acid-centric view of Mn dissolution toward a dual-site mechanism.

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