World PulseNow
Newsletter

A discrete physics-informed training for projection-based reduced order models with neural networks

arXiv — cs.LG•Monday, October 27, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
A new study introduces an innovative training framework for projection-based Reduced Order Models (ROMs) that integrates physics-informed principles. By enhancing the existing PROM-ANN architecture with a finite element method (FEM)-based residual loss, this approach effectively merges traditional ROM techniques with the capabilities of physics-informed neural networks. This advancement is significant as it moves beyond conventional methods that depend on analytical partial differential equations, offering a more robust and applicable solution in various scientific fields.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps

PlantFCE Model Builder

Build 3D process plant models with an intuitive, drag-and-drop interface.

Business & ProductivityTry the app

LucidQuery AI

Combines diffusion reasoning with autoregressive LLM for advanced AI analysis.

AI & DataTry the app

Octofy

Access all top AI models with one subscription, automatically optimized for your needs.

AI & DataTry the app
Continue Readings
CoGraM: Context-sensitive granular optimization method with rollback for robust model fusion
arXiv — cs.LG2 days ago
CoGraM: Context-sensitive granular optimization method with rollback for robust model fusion
PositiveArtificial Intelligence
CoGraM (Contextual Granular Merging) is a newly introduced optimization method designed to enhance the merging of neural networks without retraining, addressing issues of accuracy and stability that are prevalent in existing methods like Fisher merging. This multi-stage, context-sensitive approach utilizes rollback mechanisms to prevent harmful updates, thereby improving the robustness of the merged network.
Read full article
via arXiv — cs.LG
Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Field Theory Perspective
arXiv — cs.LG2 days ago
Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Field Theory Perspective
PositiveArtificial Intelligence
The introduction of the Modified Rectified Power Unit (MRePU) activation function addresses critical issues faced by deep Rectified Power Unit (RePU) networks, such as instability during training due to vanishing or exploding values. This new function retains the advantages of differentiability and universal approximation while ensuring stable training conditions, as demonstrated through extensive theoretical analysis and experiments.
Read full article
via arXiv — cs.LG
Learning to Solve Constrained Bilevel Control Co-Design Problems
arXiv — cs.LG2 days ago
Learning to Solve Constrained Bilevel Control Co-Design Problems
NeutralArtificial Intelligence
A new framework for Learning to Optimize (L2O) has been proposed to address the challenges of solving constrained bilevel control co-design problems, which are often complex and time-sensitive. This framework utilizes modern differentiation techniques to enhance the efficiency of finding solutions to these optimization problems.
Read full article
via arXiv — cs.LG
Comparison of neural network training strategies for the simulation of dynamical systems
arXiv — stat.ML2 days ago
Comparison of neural network training strategies for the simulation of dynamical systems
PositiveArtificial Intelligence
A recent study has compared two neural network training strategies—parallel and series-parallel training—specifically for simulating nonlinear dynamical systems. The empirical analysis involved five neural network architectures and practical examples, including a pneumatic valve test bench and an industrial robot benchmark. The findings indicate that while series-parallel training is prevalent, parallel training offers superior long-term prediction accuracy.
Read full article
via arXiv — stat.ML
Mixed precision accumulation for neural network inference guided by componentwise forward error analysis
arXiv — cs.LG3 days ago
Mixed precision accumulation for neural network inference guided by componentwise forward error analysis
PositiveArtificial Intelligence
A new study proposes a mixed precision accumulation strategy for neural network inference, utilizing a componentwise forward error analysis to optimize error propagation in linear layers. This method suggests that the precision of each output component should be inversely proportional to the condition numbers of the weights and activation functions involved, potentially enhancing computational efficiency.
Read full article
via arXiv — cs.LG
Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry
arXiv — cs.LG3 days ago
Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry
NeutralArtificial Intelligence
Sparse Autoencoders (SAEs) have been analyzed to determine their effectiveness in uncovering meaningful concepts within neural network representations. A unified framework has been introduced, framing SAEs as solutions to a bilevel optimization problem, which highlights the inherent biases in concept detection based on the structural assumptions of different SAE architectures.
Read full article
via arXiv — cs.LG
Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning
arXiv — cs.LG3 days ago
Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning
PositiveArtificial Intelligence
A recent study has introduced a method for verifying closed-loop contractivity in nonlinear control systems using neural networks for both controllers and contraction metrics. This approach employs interval analysis and a domain partitioning strategy to ensure that the dominant eigenvalue of a symmetric Metzler matrix remains nonpositive, which is essential for confirming contractivity. The method was validated on an inverted pendulum system, showcasing its effectiveness in training neural network controllers.
Read full article
via arXiv — cs.LG
Using physics-inspired Singular Learning Theory to understand grokking & other phase transitions in modern neural networks
arXiv — stat.ML3 days ago
Using physics-inspired Singular Learning Theory to understand grokking & other phase transitions in modern neural networks
PositiveArtificial Intelligence
A recent study has applied Singular Learning Theory (SLT), a physics-inspired framework, to better understand the complexities of modern neural networks, particularly focusing on phenomena like grokking and phase transitions. The research empirically tests SLT in various toy models, including a grokking modulo-arithmetic model and Anthropic's Toy Models of Superposition, to explore the scaling of learning coefficients with problem difficulty.
Read full article
via arXiv — stat.ML