OpenNovelty.org

Neural Force Field: Few-shot Learning of Generalized Physical Reasoning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Physical reasoningfew-shot learning
Abstract:

Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite extensive training, still struggle to achieve similar generalization, especially in Out-of-distribution (OOD) settings. This limitation stems from their inability to abstract core physical principles from observations. A key challenge is developing representations that can efficiently learn and generalize physical dynamics from minimal data. Here we present Neural Force Field (NFF), a framework extending Neural Ordinary Differential Equation (NODE) to learn complex object interactions through force field representations, which can be efficiently integrated through an Ordinary Differential Equation ( ODE) solver to predict object trajectories. Unlike existing approaches that rely on discrete latent spaces, NFF captures fundamental physical concepts such as gravity, support, and collision in continuous explicit force fields. Experiments on three challenging physical reasoning tasks demonstrate that NFF, trained with only a few examples, achieves strong generalization to unseen scenarios. This physics-grounded representation enables efficient forward-backward planning and rapid adaptation through interactive refinement. Our work suggests that incorporating physics-inspired representations into learning systems can help bridge the gap between artificial and human physical reasoning capabilities.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Neural Force Field (NFF), a framework extending Neural ODEs to learn object interactions through continuous force field representations for few-shot physical reasoning. Within the taxonomy, it occupies a unique position as the sole paper in the 'Force Field and Interaction Modeling' leaf, which focuses on explicit force representations rather than high-level dynamics or equation discovery. This isolation suggests the approach addresses a relatively sparse research direction—modeling fundamental interaction potentials directly—compared to crowded branches like Physics-Informed Loss Functions (10 papers) or Transfer Learning methods (7 papers across three leaves).

The taxonomy reveals that neighboring branches pursue complementary strategies: Physics-Informed Neural Network Architectures embed known equations into training objectives, Operator Learning discovers governing equations from data, and Representation Learning constructs latent-space encodings. NFF diverges by targeting microscopic force landscapes rather than macroscopic system behavior. The scope note for its leaf explicitly excludes general physics-informed methods and operator learning, positioning force field modeling as a distinct paradigm. Nearby work like Haptic Representation Pretraining and Operator Forces exists in other branches, suggesting connections to representation learning and operator methods but different core abstractions.

Among 20 candidates examined across three contributions, no refutable prior work was identified. The first contribution (NFF framework) examined 10 candidates with zero refutations; the second (neural operator-based force prediction) examined 9 with zero refutations; the third (interactive planning) examined only 1 candidate. This limited search scope—20 papers from semantic search and citation expansion—means the analysis captures top-ranked matches but cannot claim exhaustive coverage. The absence of refutations within this sample suggests the force field framing and few-shot integration may be relatively unexplored, though broader literature beyond these 20 candidates remains unexamined.

Based on the limited search, the work appears to occupy a sparse niche within few-shot physical dynamics, distinguished by its explicit force field representation. The taxonomy structure and contribution-level statistics indicate novelty relative to examined candidates, but the small search scope (20 papers) and single-paper leaf status warrant caution. A more comprehensive search across operator learning, physics-informed methods, and interaction modeling could reveal closer precedents not captured in this top-K semantic sample.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
20
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: few-shot learning of physical dynamics from limited observations. The field addresses how to build predictive models of physical systems when training data is scarce, a challenge that spans multiple methodological traditions. The taxonomy reveals ten major branches reflecting diverse strategies: Physics-Informed Neural Network Architectures embed known equations directly into learning frameworks (e.g., Informed Deep Learning[2], Physical Deep Learning[8]); Operator Learning and Dynamical System Identification focuses on discovering governing equations or operators from data (e.g., Ensemble-SINDy[12], Deep Transfer Operator[6]); Transfer Learning and Meta-Learning for Dynamics leverage knowledge from related tasks to accelerate adaptation (e.g., Meta-Learning PINN[13], Geometric Transfer Learning[19]); Representation Learning and Latent Dynamics constructs compact state-space encodings (e.g., Deep Dynamical Models[37], Molecular Slow Modes[22]); Control and Reinforcement Learning integrates dynamics models with decision-making; Domain-Specific Applications target particular physical contexts like groundwater flow or gear wear; Statistical and Theoretical Frameworks provide rigorous underpinnings; Data-Driven Modeling and Optimization emphasizes purely empirical approaches; Specialized Learning Paradigms explore niche settings; and Force Field and Interaction Modeling captures interaction potentials and forces at a fundamental level. Several active lines of work highlight contrasting philosophies and open questions. Physics-informed methods debate how tightly to couple neural architectures with domain knowledge versus allowing data-driven flexibility, as seen in works like Adaptive Physics-Informed Learning[20] and Dyna-PINN[21]. Transfer and meta-learning approaches explore whether few-shot generalization is best achieved through shared representations, parameter initialization, or causal invariances (e.g., Causal Domain-Invariant Dynamics[14], Koopman Few-Shot[23]). Neural Force Field[0] sits within the Force Field and Interaction Modeling branch, emphasizing the learning of fundamental interaction potentials—a direction that complements representation-focused works like Haptic Representation Pretraining[3] and operator-centric methods such as Operator Forces[29]. While many branches prioritize high-level system behavior or equation discovery, Neural Force Field[0] targets the microscopic force landscape, offering a complementary lens on how limited observations can inform physically grounded predictions.

Claimed Contributions

Neural Force Field (NFF) framework for few-shot physical reasoning

The authors introduce NFF, a framework that learns physical dynamics by representing object interactions as continuous force fields integrated through an ODE solver. Unlike discrete latent-space methods, NFF captures physical concepts like gravity and collision in explicit force fields, enabling few-shot learning and generalization to out-of-distribution scenarios.

10 retrieved papers
Neural operator-based force field prediction with ODE integration

The method uses a neural operator to predict force fields from object interactions (Equation 1) and integrates these forces via an ODE solver (Equations 2-4) to generate trajectories. This physics-grounded approach enables interpretable results aligned with established physical principles and supports both forward prediction and backward planning.

9 retrieved papers
Interactive planning through invertible force field simulation

NFF supports both forward planning by simulating action sequences to achieve goals and backward planning by inverting the ODE integration to determine initial conditions from desired outcomes. The framework enables interactive refinement where new experimental data can be incorporated to improve planning performance.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Neural Force Field (NFF) framework for few-shot physical reasoning

The authors introduce NFF, a framework that learns physical dynamics by representing object interactions as continuous force fields integrated through an ODE solver. Unlike discrete latent-space methods, NFF captures physical concepts like gravity and collision in explicit force fields, enabling few-shot learning and generalization to out-of-distribution scenarios.

Contribution

Neural operator-based force field prediction with ODE integration

The method uses a neural operator to predict force fields from object interactions (Equation 1) and integrates these forces via an ODE solver (Equations 2-4) to generate trajectories. This physics-grounded approach enables interpretable results aligned with established physical principles and supports both forward prediction and backward planning.

Contribution

Interactive planning through invertible force field simulation

NFF supports both forward planning by simulating action sequences to achieve goals and backward planning by inverting the ODE integration to determine initial conditions from desired outcomes. The framework enables interactive refinement where new experimental data can be incorporated to improve planning performance.

Table of Contents

Neural Force Field: Few-shot Learning of Generalized Physical Reasoning | Novelty Validation