Benchmark tests LLMs on discovering physics laws in unconventional worlds.
problem Difficulties in distinguishing genuine reasoning from recall in LLMs across physics evaluations.
method Interactive benchmark with 22 worlds governed by various unconventional physics laws, requiring agents to design experiments and revise hypotheses.
result Strongest agents fail on worlds requiring latent structure discovery, highlighting limitations in long-term reasoning.
Framework uses physics knowledge to improve spatiotemporal prediction with limited data.
problem Challenges in modeling physical systems with limited real-world data.
method Physics-aware meta-learning with auxiliary tasks, incorporating PDE-independent spatial and temporal modules.
result Framework outperforms in spatiotemporal prediction tasks with limited data.
3D models vulnerable to adversarial attacks, new method improves success rate and naturalness.
problem Vulnerability of 3D deep learning models to adversarial examples in the physical world.
method ε-isometric (ε-ISO) attack considering geometric properties and invariance to physical transformations. result Significantly improved attack success rate and naturalness of 3D adversarial examples.
Real-time event detection using human sensor data and adaptive machine learning.
problem Lack of effective real-time event detection using human sensor data.
method Combination of corroborative and probabilistic sources with drift adaptive machine learning.
result Automated continuous learning maintains high performance in the face of concept drift.
Forward-prediction models enhance physical reasoning, but only for specific tasks.
problem Improving physical reasoning in complex tasks involving many objects.
method Incorporated forward-prediction models into simple physical-reasoning agents and evaluated their performance on the PHYRE benchmark.
result Forward-prediction models improve physical-reasoning performance, especially on complex tasks, but generalization to new task templates is challenging.
Machine learning models in physical world are vulnerable to subtle adversarial examples.
problem Vulnerability of machine learning models to adversarial examples in physical world.
method Demonstrated vulnerability by feeding adversarial images from cell-phone camera to an ImageNet Inception classifier.
result A large fraction of adversarial examples are classified incorrectly even through a camera.
Graph networks predict and control physical systems with strong generalization.
problem Accurately predicting and controlling complex physical systems.
method Graph networks as learnable models for object- and relation-centric representations.
result Strong generalization across various physical systems.
A new method learns object hierarchies from images to reason about physical interactions.
problem Learning about the interactions of complex objects and their dynamics.
method Unsupervised learning of object hierarchies from raw visual images.
result Improves over a strong baseline at modeling synthetic and real-world videos.
Method generates audio attacks resistant to reverberation and noise.
problem Physical attacks on speech recognition models.
method Simulates playback and recording transformations to generate robust adversarial examples.
result Adversarial examples can attack speech recognition without being noticed by humans.
New method identifies physical constants from video data alone.
problem Identifying physical constants from video data.
method Proves level-set slope-coverage condition ensures local affine mapping to true physical state, enabling exact parameter recovery.
result Underdamped systems identifiable from a single video clip, other regimes require three diverse trajectories.
Improved method using filtered PDEs for robust physics-informed deep learning.
problem Complex real-world problems with noisy and sparse data.
method Proposed a surrogate constraint (FPDE) to filter and reduce the influence of noisy and sparse observation data.
result FPDE models converge better and produce higher quality solutions with less data.
Physics-informed denoising improves sensor data accuracy without needing clean data.
problem Noise in real-life sensor data affects system performance and reliability.
method Physics-informed denoising model that uses algebraic relationships between sensor measurements governed by physical laws.
result Achieved state-of-the-art performance in various real-world applications.
Soft geometric bias improves physical dynamics predictions.
problem Learning physical dynamics with exact group equivariance can degrade performance.
method Object-centric world models using geometric algebra neural networks.
result Soft geometric inductive bias leads to better physical fidelity predictions.
Physics insights into deep learning mechanisms.
problem Understanding the internal workings of deep learning models.
method Examining deep learning from quantum mechanics, statistical physics, and physical world perspectives.
result Provides new theoretical insights and practical applications for deep learning.
IDS integrates physics engines into deep learning for efficient, interpretable system identification.
problem Lack of generalization and interpretability in learning-based models of physical systems.
method Interactive Differentiable Simulation (IDS) that allows efficient, accurate inference of physical properties.
result Automatic task-based robot design and parameter estimation for nonlinear dynamical systems.
Physics-informed neural networks improve model accuracy and efficiency.
problem Accurate dynamic models for technical systems are hard to achieve.
method Physics-informed neural ordinary differential equations (PINODE) integrating Lagrangian mechanics.
result Hybrid model combines physical insight and data approximation.
Alternative finance models from physics for non-equilibrium systems.
problem Inequities of classical finance models in physics-based perspective.
method Physics-based insights for non-equilibrium finance models.
result Alternative models for non-equilibrium finance systems.
Novel neural likelihood ratio estimation for negative data in particle physics.
problem Estimating likelihood ratios with negative probability densities and weights.
method Introducing a novel loss function and a new model architecture based on signed mixture models.
result Demonstrated improved estimation on a real-world example from particle physics.
This work improves AI's ability to solve physical tasks by optimizing world models in abstracted spaces.
problem Developing AI agents capable of solving diverse physical tasks and generalizing to new environments.
method Investigates and optimizes a family of joint-embedding predictive world models (JEPA-WMs) for efficient planning in abstracted spaces.
result Proposes a model that outperforms two established baselines in both navigation and manipulation tasks.
CAZSL learns to generalize physical interactions from context.
problem Designing models that can generalize over unknown objects during manipulation.
method Context-aware zero-shot learning using Siamese network architecture, embedding space masking, and regularization.
result CAZSL models can generalize to different parameters or features of interacting objects.
Symbolic grounding in causal dynamics achieves near-infinite temporal consistency.
problem Achieving linear identifiability in non-Gaussian physical systems.
method Physics-Grounded Symbolic Architecture (PGSA)
result PGSA achieves exact linear identifiability for all physical regimes.
This paper proposes a new method to connect language and physical actions in reinforcement learning.
problem Connecting linguistic representations to the physical world in embodied agents.
method Language-conditioned goal generators to decouple sensorimotor learning from language acquisition.
result Agents can demonstrate a diversity of behaviors for any given instruction.
Improved deep dynamics models with symmetries for better accuracy and generalization.
problem Limited physical accuracy and inability to generalize under distributional shift in deep learning dynamics models.
method Incorporating symmetries into convolutional neural networks using various methods tailored to enforce different symmetries.
result Models robust to distributional shift by symmetry group transformations and favorable sample complexity.
Physics-guided models improve lake temperature and quality predictions.
problem Predicting and monitoring water temperature and quality in lakes.
method Combining physics-based models and recurrent neural networks with physical constraints.
result Improved prediction accuracy and scientific consistency.
GAN learns to simulate jet images in particle physics.
problem Simulating high-energy particle collisions efficiently.
method Location-Aware Generative Adversarial Network (GAN) architecture.
result GAN produces realistic jet images with physical properties.
Efficient attacks on DRL models without model access and low computation.
problem Vulnerabilities of DRL models to adversarial attacks.
method Adapting black-box attacks, introducing efficient online sequential attacks, exploring perturbations in environment dynamics, and generating robust physical perturbations.
result Demonstrated the effectiveness of proposed attacks on real-world robots.
ShapeShifter attacks physical object detectors with robust adversarial perturbations.
problem Crafting physical adversarial perturbations to fool object detectors.
method Adapted Expectation over Transformation technique for object detection.
result Successfully generated adversarial perturbations that fool Faster R-CNN.
Algorithm mitigates performance loss in constrained reinforcement learning with model misspecification.
problem Performance loss in reinforcement learning policies due to model misspecification in constrained control systems.
method Proposes an algorithm to handle constrained model misspecification in continuous control systems.
result Algorithm successfully mitigates performance loss in real-world reinforcement learning tasks.
Unified physics-informed learning method improves generalization performance.
problem Lack of theoretical analysis for hybrid settings with incomplete physical constraints.
method Unified residual form unifying collocation and variational methods, establishing generalization performance governed by affine variety dimension.
result Generalization performance is determined by affine variety dimension, not just the number of parameters.
New method uses neural networks for unbiased physical observable estimation.
problem Estimating physical observables with neural samplers.
method Asymptotically unbiased estimators for observables, including partition function-dependent ones.
result Superiority over existing methods in numerical experiments for the 2d Ising model.
Physics models integrated into VAEs improve generative performance and extrapolation.
problem Improving generative models with interpretability and robustness.
method Physics-based latent space in VAEs with regularized learning to balance physics and neural network components.
result Generative performance and extrapolation improvements demonstrated on synthetic and real-world datasets.
This work combines machine learning with physical models to solve inverse problems efficiently.
problem Solving inverse problems in the presence of missing physics and recovering parameters.
method Variational autoencoding with a physically structured decoder network and stochastic local approximations.
result The method accelerates inference for Bayesian inverse problems and acts as a regularizer encoding prior physical information.
A world sheet in anti-de Sitter space is a timelike submanifold consisting of a one-parameter family of spacelike submanifolds. We consider the family of lightlike hypersurfaces along spacelike submanifolds in the world sheet. The locus of the singularities of lightlike hypersurfaces along spacelike submanifolds forms …
A model learns object representations for physical scene understanding without direct supervision.
problem Learning object-centric representations without direct supervision of object properties.
method Object-Oriented Prediction and Planning (O2P2) model that learns perception, physics interaction, and rendering functions.
result The model can predict physical interactions and build block towers more complex than those seen during training.
Prototype real-world RL environment for robotics training.
problem Lack of common real-world RL benchmark.
method Developed OffWorld Gym with open access to real-world robotics environments.
result Baseline results in navigation task on real and simulated terrain.
Hybrid model combines physics and data to handle incomplete systems.
problem Incomplete physics models with missing terms.
method Combines deep grey-box models with Optimal Transport.
result Enhances incomplete physics models with superior performance.
This article reviews ∞-bundles and their applications in geometry and physics.
problem Understanding higher bundles in geometry and physics.
method An ∞-categorical formulation of higher bundles. result Identification of higher bundles in various contexts.
PhICNet combines physics and deep learning for forecasting and source identification in dynamical systems.
problem Forecasting and identifying unobservable external sources in spatio-temporal dynamical systems.
method Physics-Incorporated Convolutional Recurrent Neural Network (PhICNet).
result PhICNet can forecast dynamics and identify sources for relatively long periods.
Study benchmarks RL algorithms on real robots, revealing their performance and hyper-parameter sensitivity.
problem Lack of benchmark tasks and source code for reinforcement learning on physical robots.
method Introduced benchmark tasks with multiple robots, tested 4 RL algorithms, analyzed hyper-parameter sensitivity.
result Some RL implementations can be applied to physical robots with proper setup, but hyper-parameters need re-tuning.
AutoEncoder smooths noisy sensor data and interpolates missing values.
problem Noisy sensor data and missing timepoints require interpolation.
method Uses AutoEncoder to denoise and interpolate data.
result AutoEncoder improves data quality and reveals dynamics.
New research tackles RF deep learning adversarial examples and their physical robustness.
problem Detect and mitigate adversarial examples in RF deep learning.
method Statistical tests on PAPR and softmax outputs.
result Viable mitigation methods for RF adversarial attacks.
Graphical physics network learns intuitive physics using deep reinforcement learning with intrinsic motivation.
problem Teaching intuitive physics to AI agents.
method Integrates deep reinforcement learning with intrinsic reward normalization for efficient learning.
result Agent effectively learns object positions and velocities using intrinsic motivation.
Visualizes futures markets using particle physics tools.
problem Understanding high-velocity data in futures markets.
method Uses ROOT, an open-source data-analysis tool, to reconstruct and visualize message-based data.
result Allows stakeholders to gain a better understanding of markets and monitor effectively.
A method to automatically and symbolically detect and resolve degenerate parameter combinations from parameter-data pairs.
problem Identifying degenerate parameter combinations in physical models or real-world datasets.
method The degeneracy distillery method detects and resolves degenerate parameter combinations from parameter-data pairs.
result The method reduces the simulation budget required for downstream neural posterior estimation.
Optimizes machine learning and system identification for real-world physical systems.
problem Estimating parameters in complex, real-world physical systems.
method Combines classical system identification and modern machine learning techniques using optimization-based approaches.
result Developed regularization strategies to incorporate prior knowledge into flexible models.
New GP model tackles physics constraints efficiently.
problem Lack of efficient, physics-informed models for complex systems.
method Physics-informed variational state-space Gaussian process.
result Efficient spatio-temporal modeling with improved performance.
Novel method controls complex physical systems over long time frames.
problem Controlling complex nonlinear physical systems over long time frames.
method Hierarchical predictor-corrector scheme with separate planning and control networks.
result Successfully controls complex physical systems like incompressible Navier-Stokes equations.
A new model captures car-following and lane-changing behaviors in traffic.
problem Modeling stochastic microscopic traffic behaviors.
method Physics regularized Gaussian process (PRGP) approach.
result The proposed model outperforms previous methods in estimation precision.